The characterisation of natural fracture networks using
outcrop analogues is important in understanding subsurface fluid flow and
rock mass characteristics in fractured lithologies. It is well known from
decision sciences that subjective bias can significantly impact the way data
are gathered and interpreted, introducing scientific uncertainty. This study
investigates the scale and nature of subjective bias on fracture data
collected using four commonly applied approaches (linear scanlines, circular
scanlines, topology sampling, and window sampling) both in the field and in
workshops using field photographs. We demonstrate that geologists' own
subjective biases influence the data they collect, and, as a result,
different participants collect different fracture data from the same
scanline or sample area. As a result, the fracture statistics that are
derived from field data can vary considerably for the same scanline,
depending on which geologist collected the data. Additionally, the personal
bias of geologists collecting the data affects the scanline size (minimum
length of linear scanlines, radius of circular scanlines, or area of a window
sample) needed to collect a statistically representative amount of data.
Fracture statistics derived from field data are often input into geological
models that are used for a range of applications, from understanding fluid
flow to characterising rock strength. We suggest protocols to recognise,
understand, and limit the effect of subjective bias on fracture data biases
during data collection. Our work shows the capacity for cognitive biases to
introduce uncertainty into observation-based data and has implications well
beyond the geosciences.
Introduction
Natural fracture networks exert a strong control on the hydrogeological and
mechanical properties of a rock mass and are useful indicators of
paleo-stress directions. Geological models that depict the spatial
distribution and nature of a fracture network rely on input data (either
distributions or mean values) of fracture statistics to provide a
geologically reasonable model of the subsurface. Models such as discrete
fracture networks (DFNs) may be used for estimating upscaled permeability
(e.g.
Bigi et
al., 2013; Min et al., 2004) or for rock mechanics analysis
(Harthong et al., 2012; Jing and
Hudson, 2002), with applications including understanding fluid flow in
tight oil and gas reservoirs (Aydin, 2000) and hydrogeology
(Comerford et al., 2018), and assessing rock
strength for mine engineering (Mas
Ivars et al., 2011). There are four methods for characterising natural fractures in
outcrops: linear scanlines (Priest, 1993;
Priest and Hudson, 1981); circular scanlines
(Mauldon et al.,
2001; Rohrbaugh et al.,
2002); topology
sampling (characterising node types;
Manzocchi,
2002; Sanderson and Nixon, 2015, 2018); and tracing out the fracture network
(window sampling; Wu and Pollard,
1995). These methods handle orientation, censoring or truncation biases
(Mauldon
et al., 2001; Zeeb et al., 2013), and heterogeneity in the fracture network
(Watkins et al., 2015) with different degrees of
success. Here, we explore how each of these methods is susceptible to
subjective uncertainties related to observer biases. Furthermore, we
characterise how much the degree of variability introduced by subjective
uncertainties is dependent on the method of data collection.
Uncertainties in geological data can be broadly split into objective and
subjective uncertainty (Tannert et al., 2007). Objective
uncertainty (also called external, aleatory inherent, structural, random, or
stochastic uncertainty) refers to more traditional concepts of uncertainty,
such as precision or processing error in a technique or a dataset, and can
be represented through error bounds. Subjective uncertainty (also called
epistemic, knowledge, functional, or internal uncertainty) arises from the
mind; that is, it stems from biases that affect how individuals perceive,
gather, and interpret geological data
(Bond et al., 2015). Subjective
uncertainty is common in geosciences for which developing geological models
typically relies on extrapolation of sparse data
(Wood and Curtis, 2004), but its
magnitude and impact are difficult to quantify
(Bond et al., 2015).
The collection of fracture attributes will be affected by subjective biases.
Depending on the aims of a study (e.g. determining the connectivity and
permeability of the fracture network, determining the strength of a fractured
rock mass, understanding paleo-stress conditions), these attributes could
include the following: the number of fracture sets; the orientations, trace lengths, degree of
clustering, and aperture of the fracture population in a set; and the
topology and intensity of the network
(Jolly and
Cosgrove, 2003; Lei et al., 2017; Watkins et al., 2015). The presence and
amplitude of these biases may also be affected by the study medium. For
example, previous work has investigated the operator, used here to describe
the person undertaking the interpretation, variability in extracting lineament
or landform data from remote sensing (e.g. Landsat imagery or aerial
photographs)
(Burns et al.,
1976; Burns and Brown, 1978; Huntington and Raiche, 1978), digital elevation
models
(Hillier et al., 2015), and lidar datasets
(Scheiber et al., 2015). Differences in operator
interpretations can occur due to (a) technical factors in data acquisition,
for example bandwidth for Landsat data, image quality for aerial
photographs, or illumination direction for lidar, (b) the scale of
observation, for example 1:20000 compared to 1:5000, and (c) inter-operator differences (i.e. human factors). Scheiber et al.
(2015) found inter-operator replicability
to be poor for bedrock lineaments interpreted from airborne lidar by six
operators. Significant variability was observed in the number, trace length,
and orientation of the reported lineaments. Burns et al. (1976) attributes a difference of 8 % in interpretations to
“human factors” for lineaments identified using aerial photography. While
differences in inter-operator interpretation have been previously identified,
the underlying human factors causing these differences remain unclear. It is
also unclear how such factors affect the collection of fracture data either
in the field or from field photographs.
In this study, we investigate the magnitude and source of subjective
uncertainty in fracture data collected by linear scanlines, circular
scanlines, fracture topology, and window sampling. Fracture data were
collected from Carboniferous rocks cropping out near Whitley Bay,
Northumberland (UK), in two phases: (1) in the field where 7 participants
collected fracture data directly from the outcrop and (2) two classroom
workshops during which 29 participants with different levels of geological
training and expertise collected fracture data from field photographs. In
both the field and classroom, the participants collected fracture data
individually and in small groups. We compare the values collected by
individual participants for the same sample (scanline, circle, window sample,
etc.). It is the values as reported by the participants rather than the
underlying statistics of the measured fracture networks that are the focus of
this work. We quantify and compare the scale of subjective uncertainty for
each method and identify “problem areas” or factors that amplify the
subjective uncertainty. We consider the effect of variations due to
subjective uncertainty on fracture statistics derived from the data and
propose a number of protocols to limit operator bias in collaborative work.
Fracture data collection and analysis
Linear scanlines are a quick and relatively simple way of systematically
collecting fracture data
(Agosta
et al., 2010; Bigi et al., 2015; Chesnaux et al., 2009; Guerriero et al.,
2011; Ortega et al., 2006; Tóth, 2010). This method was developed in
rock engineering for a quantitative description of discontinuities in rock
masses (Priest, 1993) and then adopted to describe natural
fracture networks
(Becker
and Gross, 1996; Van Dijk et al., 2000; Newman, 2005; Peacock and Sanderson,
2018). The method involves laying out a tape measure on the outcrop and
measuring both the number (N) and the attributes of fractures which
intersect the scanline (e.g. orientation, spacing, length above and below
the scanline, aperture, type of terminations, filling, or mineralisation)
(Priest, 1993; Priest and Hudson, 1981). To
fully sample a fracture network, multiple linear scanlines should be
completed with different orientations, and the Terzaghi correction should be
applied to reduce orientation bias
(Mauldon and Mauldon, 1997; Terzaghi,
1965). The goal is to collect enough data to obtain a statistical
distribution for each of the main fracture parameters rather than a mean
value. It has been recommended that over 225 fractures should be sampled by
the population of linear scanlines for the method to accurately estimate the
characteristics of a fracture network
(Zeeb et al., 2013).
Circular scanlines provide estimates of fracture attributes based on the
number of fractures intersecting a circular scanline, n, and the number of
fracture trace end points, m, within a circular window
(Mauldon et al.,
2001; Rohrbaugh et al., 2002). The fracture density, intensity, and an
estimate of mean trace length for the scanline can be calculated from the n
and m values (Mauldon et al., 2001). To
be statistically valid, the number of fracture end points (m) should exceed
30 (Rohrbaugh et al., 2002); however, values
between 20 and 30 can also be considered reliable (Procter
and Sanderson, 2017). This rule defines the radius of the scanline as a
function of fracture density and limits the use of the technique in areas of
poor exposure and low-density fracture networks. A circular scanline is a
maximum likelihood estimator (Lyman, 2003) and does not
suffer from the same orientation biases observed in linear scanlines
(Mauldon et al., 2001). Circular
scanlines are ideal for rock masses with evenly distributed fracture
attributes, but may need to be combined with other methods to give a true
representation of the heterogeneity of the fracture network
(Watkins et al., 2015).
Fracture topology describes a fault or fracture network as a series of
branches and nodes (Manzocchi,
2002; Sanderson et al., 2018; Procter and Sanderson, 2017; Sanderson and
Nixon, 2015; Laubach et al., 2018). A
branch is a fracture trace with a node at each end that can be classified as
terminating into rock at i nodes (unconnected terminations), abutting
against another fracture at a y node, or crossing another branch at an
x node. Topology may be combined with circular scanlines by assessing the
nodes present within the circular window and using the sum of i and y nodes as the number of trace end points (m value) in the circle (Procter and
Sanderson, 2017). The relative frequencies of different node types (i, y, and
x) can be plotted on a triangular diagram for the purposes of characterising
and quantifying the connectivity of a fracture network
(Manzocchi, 2002; Sanderson and
Nixon, 2015).
Finally, window sampling is a technique in which all fractures within a given
sample area (window) are traced out either by hand or on a computer, and
the resulting traces are used to calculate the fracture statistics
(Pahl, 1981;
Priest, 1993; Wu and Pollard, 1995). This technique is often utilised to
analyse remote sampling data such as aerial photographs
(Healy et al., 2017), unmanned
aerial vehicle (UAV) images (Salvini et al.,
2017), bathymetry
(Nixon et al., 2012),
or satellite imagery (Koike et al.,
1998), as well as in outcrop studies
(Belayneh et al., 2009). It has been
suggested that a minimum of 110 fractures need to be sampled to
statistically describe the fracture network using window sampling
(Zeeb et al., 2013).
Summary and definition of fracture statistics that can be derived
from methods used in this work. Table adapted from Zeeb et al. (2013).
Ni: number of i nodes, Ny: number of y nodes, Nx: number of x nodes,
r: radius of circular scanline, N: number of fractures, A: area,
n: number of fracture intersections with the scanline (either linear or
circular), L: length of scanline, s: spacing between adjacent fracture
traces on the scanline, tl: individual fracture trace length, F: fracture abuts against another fracture, R:
fracture terminates into rock
(some authors also distinguish strata-bound fracture terminations).
“Yes” for trace length distribution and network topology indicates that the method can be used to carry out the technique.
Fracture statisticNotationDefinition (unit)Input parameters and calculation LinearCircular scanlineWindowsamplingDensity (D)Areal (P20)Number of fractures–D=(Ni+Ny)2πr2D=NAper unit area (m-2)Intensity (I)Linear (P10)Number of fracturesI=nL=1SI=n4r–per unit length (m-1)Areal (P21)Fracture length per––I=∑tlAunit area (m × m-2)Spacing (S)LinearSpacing betweenS=∑s(N-1)=1I––fractures (m)Mean trace lengthTlMean fractureTl =∑lNTl =n(Ni+Ny)×πr2Tl =∑lN(Tl)length (m)Network topologyTopologicalDefining fracture nodes–YesYessamplingas I, y, and x.ConnectivityUsing nodePercentage of–Pc =3Ny+4NxNi+Ny+NxPc =3Ny+4NxNi+Ny+Nxtopology (Pc)connected branchesUsing trace endPercentage ofPf =FR+F×100––classification (Pf)connected fracturesTrace lengthTl distribution (tl)Distribution of individualYes–Yesdistributionfracture trace lengths
Using these four methods, fracture parameters can be collected to calculate
key fracture statistics, for example trace length (mean and distributions),
fracture abundance (intensity and density), and connectivity (summarised in Table 1).
Trace length and trace length distribution are key fracture parameters for
DFN simulations (e.g. in simulating fracture-hosted fluid flow. Trace
lengths may be measured directly with the linear scanlines and widow
sampling or estimated using the circular scanline method). Challenges to
determining the trace lengths of individual fractures include the scale of
observation used to collect the data
(Zeeb et al., 2013), classification of
fracture intersections (Ortega and Marrett, 2000),
and the fracture fill properties (Olson et al.,
2009). Mean trace length is a commonly used fracture statistic and is useful
when the fractures in a network are evenly distributed
(Mauldon et al., 2001). However,
fracture modelling typically uses a statistical distribution representative
of the fracture length population rather than the mean (Neuman,
1993). Trace length distribution, obtained from measuring individual
fractures, should be used when investigating subsurface fluid flow or
characterising spatial variations in fracture trace length
(Watkins et al., 2015). We investigate the impact
of subjective bias on mean trace length for all four methods, including the
range of reported trace lengths for linear scanlines and window sampling and
trace length distribution for window sampling.
The characterisation of fracture networks and comparison of techniques are
greatly confounded by inconsistencies in terminology. Because fractures may
be sampled using techniques which are either 1-D (scanlines,
boreholes), 2-D (maps, surface exposure), or 3-D (rock
volumes), numerous different methodologies and terminology have arisen to
characterise the abundance of fractures in a network. One of the most widely
used methods to characterise a network is to define the number of fractures
(N) normalised to line length (L), sample area (A), or sample volume (V)
depending on the dimension of sampling. In the literature, this statistic is
either termed fractureintensity (I) or fracture frequency (f) (Sanderson and Nixon, 2015). For linear scanlines,
fracture spacing can be regarded as the inverse of fracture intensity for a single set of subparallel
fractures (Sanderson and Nixon, 2015).
Fracture abundance within a network may also be expressed as the total trace
length per unit area (Dershowitz and
Einstein, 1988; Rohrbaugh et al., 2002). This statistic is either termed
fracture intensity (Sanderson and Nixon, 2015) or fracture density
(Nixon
et al., 2012; Zeeb et al., 2013). One attempt to simplify the use of terms
is to use the Pxy terminology as defined by (Dershowitz
and Einstein, 1988), where x denotes the dimension of the sampling region (1= line, 2= area, 3= volume) and y donates the dimension of the
feature (0= number, 1= length, 2= area, 3= volume). For the
purposes of our study, we use the term fracture intensity (I) to refer to the number of fractures
per line length (P10, for linear scanlines) or fracture length per unit area
(P21, for circular scanlines), and we use fracture density for the number of fractures per unit
area (P20) (Table 1).
It is also important to understand how individual fractures relate to each
other, particularly how the individual fractures connect and hence
contribute to the strength or fluid flow through the rock mass. The number
of connections on a fracture trace (CL) is a commonly used measure of
connectivity (e.g. Manzocchi, 2002). However, a fracture
network consisting of only y and x nodes could have different CL values
depending on the fracture intensity (Sanderson and Nixon, 2015). It has been
suggested that it is better to either consider the average number of
connections per branch (CB) (Ortega and
Marrett, 2000) or the proportion of connected nodes (Pc) (Sanderson and
Nixon, 2015). In our study, we use the proportion of connected nodes for
circular scanline and window sampling. To measure connectivity in linear
scanlines, the percentage of connected fracture trace ends is reported
(Table 1).
The field site is located in the Northumberland Basin, just north of Whitley
Bay, NE England (Fig. 1). The Northumberland Basin is a 50 km wide, ENE–WSW-trending half-graben formed during middle to late Carboniferous extensional
reactivation of the underlying Iapetus Suture
(Chadwick et al., 1995; Johnson, 1984). The
stratigraphy consists of thinly (centimetres to decimetres) bedded sandstones, siltstones,
shales, seat earth, and coals of the Middle Coal Measures (Westphalian B).
At the field site the easily accessible and well-exposed wave-cut platform
clearly exhibits two sets of faults and sub-vertical joints (> 75∘) which trend E–W to NE–SW and N–S, respectively.
Summary of circular (C) and linear (L) scanlines completed in the
field and workshops (WS1 and WS2). Whether these were completed
individually (i) or in groups (g) is noted. “Order” refers to the order the
scanlines were completed in the workshops. Four of the circular scanlines
(C2, 3, 4, 5) were completed both in the field and in the workshop, but none of
the linear scanlines were completed in both, due to workshop time
constraints. Window sampling, whereby participants drew out the interpreted
fractures as well as completing topological sampling, was only completed by
Participants 1, 3, and 11 and all of Workshop 2 (WS2). The workbooks used in
this study are supplied in the Supplement (S3 and S4).
Method Field Workshop Length or radiusCompleted?igCompleted?igOrder(m)CircularC1✓✓✓ (WS1 & 2)✓31.0C2✓✓×1.0C3✓✓✓ (WS1 & 2)✓51.0C4✓✓✓ (WS1 & 2)✓41.0C5✓✓✓ (WS1 & 2)✓21.0C6✓✓×0.73C7✓✓×1.21C8×✓ (WS1 & 2)✓10.5LinearL1✓✓×1.0L2✓✓×1.0L3✓✓×15.0L4✓✓×7.5L5×✓ (WS1 & 2)✓6.55L6×✓ (WS1&2)✓1.45Window samplingC1✓ P 1, 3, 11 & WS2✓30.5C3WS2✓51.0C4WS2✓41.0C5✓ P 1, 3, 11 & WS2✓20.5C8✓ P 1, 3, 11 & WS2✓10.5Fracture data collection procedure
Six linear scanlines were set up by laying out a tape measure on sandstone
beds, both in the map and cliff section (Fig. 1c). Participants were asked to
identify for each fracture (a) the intersection distance along the tape and
(b) the length and termination (into rock, abutting against another fracture,
or not seen or obscured) of the fracture either side of the tape. Eight
circular scanlines were drawn with chalk directly onto the sub-horizontal
bedding planes of three separate decimetre-thick medium-grained sandstone
beds (Fig. 1d). The location and radius for all circular scanlines, apart
from C6, were selected by the lead author (Participant G/11) in order to
represent what they believed to include a statistically significant number
of fracture terminations (i.e. m < 30; Table 2). C6 was selected by
Participant F.
An N arrow as well as N–S and E–W lines were drawn onto the circle to aid observation.
Participants counted the number of intersections with the circumference (n).
Following the methodology of Procter and Sanderson (2017),
participants were asked to identify the number of i, y, and x nodes within
the circles. Finally, window sampling was conducted by tracing out the
fracture networks on photographs of the circular scanlines in the workshops.
Our study did not aim to collect sufficient fractures to represent the
fracture network at the field site, and the tested scanlines were not
designed to be statistically representative.
Fieldwork was undertaken by seven participants (labelled A–G) in July 2018 with
fracture data collected using field notebooks from seven circular and four linear
scanlines (Table 2). There was no particular guidance as to how the
participants collected the scanline data, but no more than one person or one
group collected fracture data from a scanline at any one time to
avoid influencing the data collected by other participants. For the same
reason, participants did not annotate or disturb the rock or scanline.
Orientation and aperture data were also measured in the field, but they are
not included in this study because they are generally not included in
circular scanline methods and cannot be measured from field photographs in
the workshops. Three of the fieldwork participants also completed the
workshop tasks (Participant C = Participant 8; Participant D =
Participant 10; Participant G = Participant 11).
Workshop 1 (WS1) was held in September 2018 in Glasgow, with 11 participants
(labelled P1–11). Workshop 2 (WS2) was held in October 2018 in Rome with 18 participants (P12–29). Participants were recruited from the authors'
research groups (the Faults and Fluid Flow research group within the Centre
for Ground Engineering & Energy Geosciences at the University of
Strathclyde and the Tectonics and Fluid Chemistry Lab of the Earth Science Dept.
at Sapienza) and colleagues from their departments: participation was
voluntary and all data were anonymised for analysis. Each two-part workshop
lasted 3 h. In the first part, participants worked individually to
complete three circular and one linear scanline, and in the second part, they worked in
small groups to complete two circular and one linear scanline (Table 2).
Participants were provided with A3 (29.7×42.0 cm) colour photographs of the
scanlines. WS1 participants were encouraged to annotate these with the
observed fracture intersections and interpreted termination type, whereas
WS2 participants were specifically asked to trace out the interpreted
fracture network (i.e. to undertake window sampling). Both workshops enabled
us to investigate the impact of subjective bias; however, the fracture maps
from WS2 enabled us to examine the impact on window sampling along with
investigating the root cause of differences for participant classification
of nodes.
Summary of the level of geological training, and experience in
geological fieldwork and fracture data collection, reported by field and
workshop (WS) participants. Individual participant responses are provided in
the Supplement (S2).
Geological training Familiarity with geological fieldwork Familiarity with collecting fracture data GroupNo. of participantsNoneLowMediumHigh(Other)NoneLowMediumHigh(Other)NoneLowMediumHigh(Other)Field7103301033010330WS111223222151232510WS218306903636065520
To examine the effect of geological experience on subjective uncertainty,
participants were asked to indicate their level of geological training,
familiarity with geological fieldwork, and their level of experience
collecting fracture data (summarised in Table 3, questionnaire provided in
Supplement S1). In the workshops, a small number of
participants (Participants 2, 5, 24, and 28) consistently reported
anomalously high n values compared to the node counts. Three of these
participants (Participants 2, 5, and 28) had no formal geological training or
experience in geological fieldwork and fracture data collection. It is
possible that these participants only considered fractures that intersected
the edge of the circle in their interpretation, neglecting fractures within
the circle that do not intersect the circumference and introducing a
different source of subjective error.
Post-workshop analysis
For the workshop data, we digitised the interpreted fracture traces and node
classification for all participants who traced the networks (see Table 2)
using ArcGIS. Individual fracture trace lengths for all scanlines, and the
distance along the scanline that each fracture intersected linear scanlines,
were exported as “Arcmap unit” lengths. These lengths were then scaled to
the field to enable comparison of the fracture statistics. In some cases,
the counts of n or node types reported by participants differed from the
count indicated on the worksheet (see S7). In these cases, to be consistent
with field data collection, we take the value reported by the participant.
Digitised networks from Circle 8 were used as a case example to (a) construct heat maps of point density for n, i,
y, and x nodes, as well as line
density for fracture traces, and (b) identify areas within the circular
scanline with the greatest variability in the identification and
quantification of fracture characteristics such as trace, node type,
and termination.
Fracture statistics were calculated for the data populations from the
different fracture characteristics that were measured or counted and were then
investigated as a function of the field and workshop participants.
We report the impact of subjective bias for the following fracture
statistics: fracture intensity (I), fracture density (d), the connectivity of the network (Pc and Pf), mean trace length (Tl),
and trace length distributions (tl). Statistics are calculated using the
equations outlined in Table 1.
In theory, each of the scanlines have a “true” value for each of the
fracture parameters (number and type of fracture intersections and
terminations, i.e. n, Ni, Ny, and Nx). In this paper, we are not interested
in defining that true value; rather, we wish to explore the ranges in
reported values from different participants, showing the scale of subjective
bias for the collected data and the factors that affect this range.
Therefore, we define the uncertainty, or level of variability, present in
fracture data collection and the related statistics as a function of the
observers or operators.
Analytical framework
We describe the quantitative fracture data that the participants collected
using the following approaches.
Spatial distribution and node triangle space. Several fracture
attributes are determined by the spatial distribution of features, e.g.
fracture traces, within a sample area. For linear scanlines, we visually
determine the relative location of interpreted fracture traces from the
digitised data. For circular scanlines, the spatial distribution of nodes is
represented via point density heat maps generated from digitised data in
ArcGIS and used to identify areas of uncertainty. We also visually compare
the participants' interpretation using node triangle plots. For example, for
all circular scanlines, we compare the relative position of node data
interpreted for each participant.
Range and variability. The spread of data is described using the range
between the minimum and maximum value for a given parameter or statistic
(e.g. fracture count) and the quartile-based coefficient of variance (QCV;
Eq. 1).
QCV=Q3-Q1Q2
QCV is interpreted in a similar manner to the standard coefficient of
variation (CV) and provides a dimensionless measure of variability which can
be used to compare between scanlines and attributes. QCV is more appropriate
than the standard CV for this study because many of the data do not display
a normal distribution. Further, the median and IQR are less susceptible to
being skewed by outliers. We describe variability using the following
descriptors: very low (QCV =0.00 to 0.10), low (QCV 0.11 to 0.25), moderate (0.26 to 0.50),
large–high (QCV =0.51 to 0.70), very large–high (QVC =0.71 to 1.00), and extreme
(QCV > 1.01).
Covariance. We describe the strength of the relationship between
quantitative data (e.g. fracture count and time taken) using the linear
coefficient of correlation (R2). Trends are described using the
following descriptors: no (R2 < 0.35), very weak (R2 0.35 to 0.50),
weak (R2 0.51 to 0.70), moderate (R2 0.70 to 0.9), and strong
(R2 > 0.90).
Consistency. Consistency can be used to describe two different
aspects of the data. First, it can describe the rank position of
participants for a specific reported (e.g. n point count) or calculated
(e.g. fracture intensity) value across all scanlines. In this case, high
consistency would describe a participant that remains within three rank
positions for a reported or calculated value for all circles. In contrast,
low consistency would describe a participant who ranks highly in one
scanline and low in another. Consistency uses descriptors depending on the
range in rank position across scanlines as follows: no (> 16 rank
positions for individual and > 6 for group exercises), low (15 to 11
rank positions for individual and 4 to 6 for group exercises), moderate (7 to 10
rank positions for individual and 2 to 4 for group exercises), and high
(< 7 rank positions for individual and < 2 for group
exercises). Consistency is also used to describe the range and variability,
quantitative data, or visual assessments across all scanlines within a
method.
For qualitative data, such as the degree of experience with
collecting fracture data, statistical interrogation is not appropriate
given the potential for ambiguity in the response categories; the categories
are not necessarily linear, and participants may judge “high”,
“moderate”, and “low” differently. Instead, we visually interpret trends
in qualitative data and use numerical indicators, such as the range or
median, to interpret trends across participant responses and their
interpretation.
Summary table of raw linear scanline results, where i: individual, G: groups, No.: number of participants or groups.
ScanlineIndividual orNo.Fracture count Trace length (m) Time (min) groupMinMaxMedianQCVMinMaxMeanQCVMedianQCVMinMaxMedianQCVL1Fieldi63107.00.710.032.220.580.360.400.155:32a9:00a7:16a0.24L2FieldG371412.00.290.011.780.430.170.260.21––––L3FieldG3213826.00.330.0423.081.210.690.540.1810:0013:0010:000.15L4FieldG2181918.50.030.0514.42.290.611.170.69––––L6WS1i111023140.390.020.610.210.390.190.432:178:404:58b0.33WS2i18925210.380.030.720.240.280.230.271:5124:006:12c0.66L5WS1G52231220.230.122.720.860.730.700.825:579:357:330.24WS2G71528200.400.142.430.960.210.860.475:0013:008:170.57
a Only two
participants recorded time for this scanline. b P10 did not record the time taken
to count nodes. c P23 did not trace fractures, so we only have spacing and time
information.
ResultsLinear scanlines
The results of a statistical analysis of fracture data collected from linear
scanlines are shown in Table 4. The range in the number of fractures
interpreted to intersect the scanline varied between participants and
between scanlines both in the workshop and the field. For example, in the
field, QCV ranges from 0.03 for Line 4 to 0.71 for Line 1 (Table 4). The
variability in the trace length data depended on the scanline being sampled,
more so than which participant was sampling, and could be as low as 0.15
(L1) or as high as 0.82 (L5, WS1). We find that there is greater variability
in the minimum recorded trace length (high to extreme) than the maximum
recorded trace length (moderate to high). For example, for Line 6,
participants reported minimum trace lengths ranging from 0.02 to 0.23 and
maximum trace lengths ranging from 0.25 to 0.72 m (See S5). It is clear that
the interpretations by participants differed on individual fracture
terminations. For example, for one fracture intersecting Line 3,
Participants G and F interpreted that after 8.0 m the fracture
terminated against another fracture, whereas Participants C and D felt that
it terminated in an area of no exposure after 22.0 m (S5). The correlation
between the number of fractures intersecting a linear scanline and the range
of reported fracture trace lengths by participants for that scanline shows
weak to no trend in the field (e.g. R2=0.59 for Line 1) and no
trend in the workshop (e.g. R2=0.24 for Line 1). That is, our
results indicate that trace length is not correlated with the number of
interpreted fractures.
The interpreted fracture traces for Line 6 (length 1.45 m).
(a) The digitised fracture networks for all workshop participants. (b) Field
photograph of Line 6. (c) Fracture trace length histograms (bin = 0.1 m)
for participants who recorded a low to high number of fractures. The
corresponding digitised fracture trace is also highlighted in the
appropriate colour. Key differences in the interpreted fracture networks are
highlighted using participants who selected a low (Participant 28, nine
fractures), medium (Participant 10, 17 fractures), and high (Participant 14,
25 fractures) number of fractures.
The fracture traces drawn onto photographs in the workshops helped us to
understand the underlying controls on differences in interpretation. We
examined the fracture traces of Line 6 in detail and the interpreted
fracture networks can be considerably different (Fig. 2). All participants
identified two large fractures located roughly 1/3 and 2/3 of the way along
Line 6; however, participants differed greatly in their interpretations of
the first third of the scanline. Participant 28 does not identify any
fractures, whereas Participants 10 and 14 identified 3 and 10, fractures,
respectively. Such differences between participants' observations could be a
function of the site; the fractures are partly obscured by water and have
thin fracture traces. These “hairline” fractures are also present in other
parts of the scanline and in all cases increase the observation variation
between participants. Also in Line 6, a feature trending at a low angle to
the scanline halfway along was only identified by 14 of 29 (48 %)
participants. Where this feature is identified, it is also the longest
visible fracture trace that transects the scanline, so identifying this
fracture affects the trace length statistics. Our analysis suggests that the
main source of uncertainty for characterising fractures along photographs of
linear scanline is the decision on how a fracture terminates and hence how
long the fracture is interpreted to be.
Results of the fracture data from circular scanlines (C1–7)
collected in the field by seven participants (labelled A–G, though A, E, and F
did not complete all of the scanlines). (a) The number of fractures that
intersected the circular scanlines (n). (b) Fractures that terminated in
rock (i nodes). (c) Fractures that terminated against another fracture
(y nodes). (d) Fractures that intersect another fracture (x nodes).
Participants C and D repeated some of their measurements for selected
circles and this is indicated by two bars in their column for that circle.
Circular scanlines: topological sampling and fracture mapping
We present the results of circular scanlines and topological scanlines
together because participants defined nodes within sample circles for both
sets of measurements. For the circular scanlines, the number of fracture
terminations (m), although not explicitly discussed in this section, is
equivalent to the total number of i and y nodes.
The reported values for n points and topological characterisation for
circles undertaken in the field are presented in Fig. 3. The number of
fracture intersections with the edge of a circle (n) displayed very low to
low variability as recorded by the field participants (QCV ranged from 0.05
to 0.19; S7). However, there is greater spread in the number of reported
nodes identified within a circle. The scale of variance depends on the
properties of the circle that is being sampled; variance ranged from very
low for Circle 1 (QCV =0.03) to high for Circle 6 (QCV =0.62). All
node types (i, y, and x nodes) displayed a wide spread in variability,
ranging from low to extreme across different circles.
Summary of fracture data and time taken for circular scanlines 1, 5
and 8, in the field and workshop, either working individually (i) or in
groups (g). The data are presented in the order scanlines were completed in
the workshops.
n point Node count nt (s) i node y node x node t (s) i/gRangemedianQCVRangeMedianQCVRangeMedianQCVRangeMedianQCVRangeMedianQCVRangeMedianQCVC1Fieldi15–21170.1619–4229.50.390–30.53.512–21190.226–1470.32137–2301720.33WS1i14–23180.2236–99680.870–1212.01–38190.744–1170.43119–4472400.77WS2i11–2518.50.3015–295821.410–6124–34180.814–147.50.582–1140289.51.11C5Fieldi14–1915.50.1614–43210.644–850.4528–4734.50.382–83.51.07127–245165.50.32WS1i7–18120.2520–120470.783–1450.94–34200.531–620.75150–11773170.46WS2i9–18120.0820–29867.50.990–324.51.397–41140.50–111.51.1760–10502811.09C8WS1i10–25230.1729–180780.772–1150.51–60260.52–22100.3150–7803780.8WS2i16–32240.2245–2401070.481–1640.55–4519.50.925–1810.50.4830–14405990.64C4Field (i)i12–20150.1324–50410.415–19130.3120–34290.170–40–147-2151670.35WS1g11–18140.3660–330970.497–1990.226–27110.731–430.67324–5214050.16WS2g10–18140.3964–3231290.965–2350.95–27110.500–311.5115–7202901.35C3Fieldi19–30220.0524–5838.50.683–1571.021–33290.246–1680.5162-2822610.15WS1g18–2219.50.1755–9077.50.24–205.50.8619–24230.055–115.50.41208–5213220.29WS2g14–23160.1352–7131291.772–5471.4311–22180.393–1040.38143–6003600.63
Similar reporting behaviour is observed for data collected in workshops;
however, the workshop data are even more variable than field data (Fig. 4;
Table 5). When particularly large variability was observed for a
topological parameter (e.g. y nodes), it was not necessarily replicated for
the counts of other parameters (e.g. n points) for the same circle. For
example, the number of y nodes interpreted in the field varied greatly for
Circle 6 (7 to 27; QCV =0.66), even though this circle had the smallest
range in values for n points (6 to 9; QCV =0.19). In this case, clearly
the participants saw almost the same fracture intersections with Circle 6 (i.e. subjective
bias for n points is small). At the same time, the participants differed in
their observations and classifications of fracture characteristics within the
circle, leading to a greater range in the number of fracture intersections
there. The consistent observation is that subjective bias affects node
counts more than n point counts, but the degree of variability is
dependent on the sample site – i.e. the characteristics of the circle being
sampled.
Recorded fracture data (n and node counts) and the time taken to
complete n and node counts for workshop (WS) participants (P) and groups
(G). The data for each attribute have been colour-coded according to where
the reported value for the parameter ranked for that circle. Data are
presented in the order that they were completed in the workshop.
No single circular scanline was particularly prone to subjective bias for
all of the studied fracture parameters. For example, compared to other
circular scanlines, the variability in data collected from Circle 3 is small
for n points and y nodes, but it is one of the most variable for i nodes and
shows moderate variability for x nodes. In contrast, the variability in data
collected from Circle 7 is small for n points but displays high variability
in y nodes, very high in i nodes, and extreme in x nodes (Table 5). The
trends are seen in both field and workshop data.
Node triangles for workshop participants and groups. For
individual circles (a), Participants 5, 21, and 11 were highlighted to show
the consistency in the way participants classified nodes. Participants were
selected according the whether they reported a low (P5), medium (P21), or
high (P11) node count. Similarly, for group circles (b) Groups 7 and 12 were
highlighted as groups who recorded a high and low node count.
Although individual circles displayed considerable variability between
participants, many participants remained consistent in their observations
between different circles (Fig. 3 and 4). For example, Participants A, C,
and 2 tended to report lesser counts for all circles than
Participants G and 13. That said, when Participants C and D
repeated the data collection exercise for the same scanline in the field,
there were differences within the repeat data (Fig. 3), although they were far
fewer than the discrepancies between participants. The level of consistency
depends on both the participant and the attribute being measured. For example,
for circles undertaken in the workshops by individual participants, node
count displays a high degree of consistency (6.6), whereas n point count
displays moderate consistency (9.7). When individual participants are
inspected, the level of consistency between scanlines ranged from 1
(Participants 2, 3, and 13) to 19 (Participant 9). It is clear that some
participants displayed a greater level of consistency (e.g. Participant 28),
while other participants' interpretations varied from one circle to another
(e.g. Participant 9). The relative proportion of specific node
classification (e.g. y nodes) remained consistent between circles (Fig. 5).
For example, Participant 11 consistently recorded more y nodes when compared
to other participants, while Participants 5 and 21 tended to record more i
and x nodes. The same trends are seen both in field data and workshop data
collected as groups.
In general, the scale of uncertainty (the range in reported values) in the
workshop data is greater than field data as indicated by a wider range in
reported values and higher QCV. Overall, the number of fractures reported
was larger in the field data than the workshop data. For example, the
reported number of fracture intersections in Circle 3 in the field (Fig. 3)
ranged from 19 (Participant C) to 30 (Participant B), whereas from the
workshops it ranged from 14 (Group 8) to 23 (Group 6) (Fig. 4). Similarly, the
number of y nodes is generally greater in the field and the range in values
for each circle is less extreme – e.g. the number of y nodes for Circle
5 ranged from 28 (Participant C) to 47 (Participant D) in the field (QCV =0.38; Fig. 3c) and from 4 (Participant 2) to 41 (P13) in the workshops
(QCV = 0.81; Fig. 4). It is possible that in the field participants could
observe fractures in more detail (e.g. the hairline fractures in Fig. 2),
resulting in more consistency in their reported values.
In our data there was a clear discrepancy between the number of nodes or
n points reported by participants during the workshops and the number recorded in the paper
copies of interpreted circular or linear scanlines. Participants tended to
report a smaller number of nodes or n points than they had drawn on their
worksheets. While the magnitude of this error varied both between
participants and between scanlines, the differences were consistently higher
for data collected within an area (i.e. node counting) compared to those
collected along a sample line (i.e. n points). This counting error was much
more pronounced within the circle than around the edge, suggesting that as
data gatherers we are relatively good at counting when we follow a sample
line (e.g. the edge of a circle or linear scanline). However, when counting
within a sample area the accuracy of results is reduced.
Summary of fracture parameters reported for window sampling. Data
are presented in the order the scanlines were undertaken within the
workshops. The labels (i) and (g) denote whether the scanline was undertaken
individually or as a group.
For window sampling, the number of recorded fractures displayed moderate to
high variability (Table 6), with the largest variation occurring for Circle
4 (11 to 29; QCV =0.76). The maximum trace length reported by all
participants remained fairly consistent (QCV ranging from 0.01 for Circle 8
to 0.29 for Circle 1). However, considerable variability in trace length
distributions was observed between participants (Fig. 6), with the number of
small fractures recorded across all scanlines displaying the most
variability. For example, the number of fractures below 0.2 m recorded for
Circle 8 ranged from 7 (Participant 24) to 41 (Participant 11), which represents 36.8 % and 75.9 % of the
reported fractures for both participants. This is also seen in the minimum
reported trace length data, which displayed very high to extreme variability
(e.g. 0.02 to 0.11 m for Circle 4; QCV =0.94). While the number of small
fractures recorded by participants varies between circles, whether a
participant records a high or low relative percentage of small fractures
remains consistent. For Circles 8, 5, and 1, Participant 3 consistently
recorded a high percentage of small fractures, whereas Participant 24
consistently recorded a low percentage of small fractures (Fig. 6a). In
short, participants either consistently record the presence of small
fractures in a network or consistently do not record the existence of small
fractures in a network. For trace lengths longer than about 15 %–20 % of the
diameter of the circle, the shape of the distributions remains consistent
across all participants, indicating that the larger traces in the fracture
network are consistently identified independent of the participant (Fig. 6).
Fracture trace length distributions for (a) individual and
(b) group window sampling data. The results are presented as both histograms
and normalised cumulative frequency curves of fracture trace length with bin
widths of 0.05 m for individual and 0.1 m for group window sampling data.
The range in the relative percentage of small fractures observed in the data
is highlighted using participants and groups who consistently observed a
high and low percentage of small fractures (Participants 3 and 24 and
Groups 12 and 11, respectively).
Areas of increased uncertainty: a case study using Circle 8
To highlight potential causes of differences in interpretation, Fig. 7d
compares the interpretations of fracture traces and nodes in three
particular problem areas (so-called owing to how differently these parts
were interpreted) from endmember Participants 11, 18, and 21, who reported
high, medium, and low node counts, respectively. Area 1 is well exposed and
contains several intersecting fractures. The nature of the connections was
interpreted differently by each participant. Participant 21 interpreted only
the major fractures coming into the junction and depicted the fractures
in a star-like formation. Participant 18 interpreted a standard
x node, with a second larger fracture terminating against the NE–SW-trending
fracture (y node), and also notes an E–W-trending fracture linking the two
major fractures and cutting the third (three x nodes). Participant 11
differed from Participant 18 by interpreting the NE–SW fracture trace as
being offset by the NW–SE fracture such that the x node interpreted by
Participants 21 and 18 was instead interpreted as two y nodes. Area 2 is a
complex intersection of a number of NW–SE fractures with part of the
photographed exposure obscured by shadow (a clear limitation of interpreting
the scanline from photographs rather than in the field). Participant 21 did
not interpret the fractures obscured by shadow, whereas Participant 18 did.
Participant 11 depicted a number of smaller fractures which Participants 18
and 21 did not identify. Area 3 is an intersection of two large fractures
which is obscured by a coarse sand infill. Both Participants 18 and 11
interpreted the obscured connection as a simple x node, whereas Participant
21 felt that the fracture bifurcated to frame the area of no exposure.
Participants 18 and 21 interpreted the other fully exposed connections
similarly (although Participant 21 does not depict a fracture to the south
of the sand fill), whereas once again Participant 11 identifies several
additional smaller and complicated fractures and fracture connections,
particularly y nodes. In each case, it appears that participants effectively
“self-censored” their data according to their “preferred” minimum trace
length and had different approaches to areas of shadow or obscured outcrop.
The different geometry of the interpreted fracture intersections would
result in significant differences in interpreted fracture development
history.
A detailed study of the areas which cause increased uncertainty in
Circle 8. The figure comprises clean field photographs of Circle 8 with
the (a) heat map of y node point density, (b) heat map of fracture trace
density, and (c) areas identified as problem areas. In panel (d) the close-up
of areas 1, 2, and 3 along with the features recorded by Participants 11, 18,
and 21 are shown. See text for full description.
When analysing the node classifications and interpreted trace lengths for
all circles it was found that in many cases the fracture networks depicted
or interpreted were not viable: in other words, there were undefined nodes
or intersections that had an incompatible number of branches entering the
node (e.g. four nodes for a y node or five for an x node). Occurrences of these
undefined or “floating” nodes were more common in WS1 than WS2, perhaps
because WS2 participants were specifically asked to draw out the fracture
network on their photographs.
The effect of working in groups
Large variability in the number of reported fractures in the field was also
seen when linear scanlines were undertaken as pairs; for example, for linear
scanline 3 counts ranged from 21 (Participants C and D) up to 30
(Participants
A and B). The groups are obviously made up of participants who have
a different “eye for detail”. When working individually, Participants C and D
both recorded small fracture counts, while Participant B recorded the
highest. There is a suggestion in the data that when working as pairs,
groups tended towards the more detailed member; for example, Participant F
recorded the lowest fracture count when working individually. However, a
group with Participant G recorded a higher than average fracture count. This
was also addressed in the discussion following Workshop 1.
No clear differences can be seen between data collected individually and as
groups for either circular scanlines (Table 5; Fig. 4) or window sampling
(Table 6; Fig. 6b). Although the group circles have smaller y counts and
greater mean trace length values, the differences are not enough to be
confident that the effects are due to working in groups rather than
differences in the fracture network. This is due to the limited number of
circles completed, the fact no circles which were completed individually
were completed as a group, and that there is a large spread in variability
between participants and between different circles. That said, groups
generally reported more complex fracture networks with a higher reported
number of small fractures. When working as groups that included a naturally
detailed and naturally less detailed participant, the results tended to be
more detailed: compare recorded values from Participants 2 and 11 when working
individually or together as Group 3 (S7).
The impact of participant experience on the collection of fracture
data. (a) The time taken in seconds to record fracture data (n and node
counts) from circular scanlines both in the field and workshops. (b) The
impact of experience on the recorded y count and number of fractures in
individual scanlines and the time taken to complete the workshop tasks.
There is also no difference in the level of variability for any particular
parameter reported for either topological sampling within a circular
scanline or window sampling (e.g. y node count, number of fractures, etc.).
For example, node counts display QCV values of 0.48 to 1.00 for individual
circles and 0.40 to 1.00 for group circles. This suggests that working as a
group does not affect the level of subjective bias in the dataset.
Similarly to when working individually, the majority of groups show high
levels of internal consistency in the number of reported fractures (7 out of
12 groups). Groups also displayed internal consistency in the relative
percentage of small fractures (Fig. 5b) and node types (Fig. 4b) reported
across different sample circles.
Time taken to collect data
In the field, the time taken to complete counts of n points and nodes
varied not only as a function of participant, but also the circle being
sampled. It was not clear if it took longer for participants to count more
n points or nodes, with the trend being non-existent to very weak for
n points (R2 ranging from 0.003 to 0.37) and non-existent to weak for
nodes (R2 ranging from 0.04 to 0.70). For workshop data no trend was
observed between the time taken to record and the variability in the number
of reported fractures observed (Fig. 8a). Both the time taken and magnitude
of the variability were considerably greater in the workshops compared to the
field. For example, Circle 5 took participants between 1 and 17 min in
the workshop (QCV =0.94) and 2 min 21 s to 4 min 26 s (QCV =0.64) in the field.
Window sampling, which was undertaken in WS2, took longer than circular
scanlines for the same circle in WS1; however, this difference is small.
While it took 1.3 to 3.2 times as long to record n values, the relative time
taken to complete topological sampling within the circle is comparable for
circles completed both as individuals (0.85 to 1.6) and in groups (0.95 to
1.05). Thus, although circular scanlines are often suggested as a quick way
of gathering fracture data, it does not take significantly longer to trace
out the fracture network. This observation suggests that a similar amount of data
could be collected using both methods.
While some participants took much longer than others, the participants were
often (18 out of 29 participants) internally consistent in the time taken to
complete their tasks (Figs. 3 and 8). For example, C and G tended to take
longer than A or D in the field, and in Workshop 2, Participant 29
consistently took longer than Participant 25. Although this was often
observed, some participants displayed low to no consistency in time taken
between scanlines. For example, Participant 25 ranked third quickest for
Circle 8 and 28th quickest for Circle 1 in the time taken to count
n points. No correlation was found between average rank position and range
in rank position for the time taken to record n point data (R2=0.025) or node data (R2=0.001).
Experience
The relationship between experience and the number of node counts has a
large amount of scatter (Fig. 8b). Generally, participants with less
experience undertaking geological fieldwork or collecting fracture data
counted fewer nodes than more experienced participants; however, the trend is
very weak. Perhaps counter-intuitively, experience does not reduce the time
taken to collect fracture data (Fig. 8b). However, for node counts, the
fastest experts are still notably slower than the fastest inexperienced
participant. Also, more experienced participants do not appear to
characterise with more detail than those with less geological training or
experience. It is possible that participants with experience in fracture
analysis will consider the connections they observe, whereas beginners will
draw the traces that they see without considering the implications of those
connections (i.e. implied cross-cutting relationships).
Effect of subjective bias on the derived fracture statistics
The variability in the collected fracture parameters will affect the derived
fracture statistics in different ways. No particular equation for the
calculated statistics (Table 1) has a statistically sensitive relationship
with
subjective bias for a particular fracture attribute. To identify which
fracture statistics are most susceptible to subjective bias, we discuss and
compare the results from all methods in terms of the relative ranges of
values.
The effect of subjective bias on mean trace length depends on the method that the statistic is
being derived from. For linear scanlines the variability depends on the
scanline being sampled. For example, small variability is seen for Line 2,
with values ranging from 0.33 to 0.49 m (QCV =0.17) compared to 0.89 to
3.70 m (QCV =0.61) in Line 4. For topological sampling within a circular
scanline low to very high variability is observed between participants in
the field, with QCV ranging from 0.13 for Circle 3 to 0.82 in Circle 7.
Variability is higher in workshop data, for which moderate to high QCV values
are observed (0.34 to 0.72), with both group circles displaying moderate
variability (0.34 and 0.38). Mean trace length derived from window sampling
displays moderate variably across all circles sampled (QCV 0.26 to 0.47) and
displayed lower variability compared to trace length derived for the same
circle using topological sampling. Mean trace length derived from window
sampling was consistently less than that derived from circular scanlines of
the same circle. For example, the mean trace length for Circle 5 derived from
window sampling ranged from 0.19 to 0.46 m (S8).
For linear scanlines, no correlation was observed between the number of
observed fractures and fracture trace length. For example, Participants B
and G both recorded 10 fractures intersecting Line 1; however, the derived
mean trace lengths were 0.62 and 0.25 m, respectively (see S5). This
outcome contrasts with window sampling, whereby mean trace length decreases as
fracture count increases (R2=0.79 for Circle 8; see S8), and
circular scanlines, for which mean trace length is a function of the number of
fractures intersecting and terminating within a circle.
Fracture density, which is calculated for circular scanlines and window sampling, has moderate
to high variability between participants. For both methods the level of
variability depended on the circle being sampled, along with whether the
analysis was undertaken in the field or in the workshops. For example,
fracture density derived from circular scanlines ranged from 3.82 to 7.48 f/m2 for Circle 3 (QCV =0.13) up to 2.12 and 10.6 f/m2
for Circle 6 (QCV =0.68) in the field and from 2.07 to 12.1 f/m2 for Circle 3 (QCV =0.34) up
to 0.48 and 6.53 f/m2 for Circle 1 (QCV =0.79). For window sampling
participant statistics displayed moderate to very high variability within
circles (QCV 0.44–0.76). A larger value for fracture density is obtained
when window sampling is used for the same circle, as shown in Circle 8,
for which window sampling fracture density ranged from 22.9 to 68.8 f/m2
compared to 1.9 to 41.4 f/m2 for circular scanlines. Variability between
participants is lower for window sampling compared to circular scanlines
when samples are undertaken individually, but there is more variability
when undertaken as a group.
Across all methods, fracture intensity has the smallest amount of variability between
participants; however, differences are still observed between methods. When
linear scanlines are used the amount of variability depends on the scanline
being sampled. For example, Line 4 ranges from 0.93 to 0.98f/m (QCV =0.03), whereas Line 1
ranges from 2.31 to 7.69f/m (QCV =0.71), with the
majority of scanlines displaying low to moderate variability. When fracture
spacing, instead of the number of fractures reported, is used to calculate
fracture intensity more variability in values is observed, primarily due to
the large difference in the minimum reported fracture spacing by
participants across all circles. Unlike for linear scanlines, fracture
intensity represents a robust statistic for both circular scanlines and
window sampling. This is emphasised by the QCV values for circular
scanlines both in the field (QCV 0.03 to 0.21) and workshop (0.19 to 0.43),
along with those for window sampling (0.11 to 0.21). Fracture intensity
estimates using circular scanlines derived from field data generally provide
a higher value than when the same circle is analysed in the workshop. For
example, Circle 3 ranges from 4.75 to 7.5f/m from field data and 3.5 to
5.75 from workshop data. Fracture intensity derived from window sampling is
consistently lower than that derived from circular scanlines for the same
circle.
The connectivity of the network (percentage of connected fractures, Pf) is highly variable for values gathered by
participants using linear scanlines, with the magnitude of the variability
dependent on the scanline being sampled. For circular scanlines and window
sampling, for which the percentage of connected branches (Pc) is used,
connectivity represents a robust statistic with very low QCV values (e.g.
0.00 to 0.06 for field data). The maximum reported values for Pc remain the
same when field and workshop data are compared; however, the lowest reported
values are consistently lower in the workshops for any given circle.
Summary of the broad trends in fracture statistics derived from the
three methods we explored; presented in Fig. 9.
StatisticCircular scanline – topologyCircular scanline – windowLinear scanlineIntensityVery low to low variability when derived from field data and low to moderate when workshop data are used. For Circles 1, 4, and 5 the calculated intensity from workshop and field data were very similar; however, the calculated intensity for Circle 3 was much lower in the workshop. In all cases ranges are greater when workshop data are used, particularly for Circles 1 and 5.Low spread between participants within circles. In all cases, apart from Circle 4, intensity calculated using window sampling is lower than that derived for node counting for a given circle.Variability, which ranged from very low to high, depends on the scanline being sampled. For example, Lines 3–5 are all low intensity and have a small range.Density and spacingLow to high spread when derived from field data and moderate to very high when workshop data are used. Density calculated from workshop in all cases apart from Circle 1 is lower than when calculated from field data.Moderate to high spread. Values consistently higher in workshop data when window sampling data are used compared to node counting, particularly Circle 8. Can be comparable to field density (Circle 4) or considerably higher (Circle 1).Variability in mean spacing values depends on the scanline being sampled, ranging from very low to very high. Maximum reported spacing had low spread, whereas minimum spacing ranged from low to extreme variability depending on the scanline being sampled. Equally large range in workshops and field.Mean trace lengthLow to moderate spread when derived from field data and moderate to high when workshop data are used. How similar the range in reported values is between workshop and field data varies for different circles.Moderate spread across all circles. The extremes in the ranges observed in mean trace length estimates are considerably lower than for node counting. Of all methods window sampling provides the smallest estimate for mean trace length.Moderate to highly variable for most scanlines. Equally large range in workshops and field. Maximum reported trace lengths generally much larger than for other methods due to the different scale of observation.ConnectivityVery low spread between circles, methods, and settings (field vs. workshop).Not assessed separately from node classifications.Spread depends on the scanline being sampled and ranges from very low to extremely variable. Equally large range in workshops and field.
Subjective bias impacts all data collection methods (Table 7). Window
sampling appears to be the method which is least effected by subjective
bias. Out of the methods tested in the workshops, window sampling displays
the lowest variability between participants for all of the parameters:
intensity (low), density (moderate to high), mean trace length (moderate),
and connectivity (very low). Additionally, because this method requires the
network to be drawn out, it is possible to check for the existence of
floating nodes and other irregularities in the recorded fracture network.
Linear scanlines had the greatest variability between parameters.
The different fracture statistics also display different degrees of
subjective bias. Fracture intensity represents the most robust statistic as it shows the least
variability in data collected by different participants for a given
scanline. In contrast, mean trace length and fracture density both display considerable variability in the
reported data, particularly when derived from workshop data. The
connectivity of the network was found to be robust for topological sampling; however,
considerable variability existed in the values reported from linear
scanlines. When participants traced out fractures while completing linear
scanlines or window samples, it was possible for us to identify the causes
of differences in participant observations; these are differences that affect the
derived fracture statistics.
Discussion
Subjective bias in fracture data collection has implications for the
validity or reliability of the models that the data inform, such as the
derived fluid flow parameters, rock strength characteristics, or paleo-stress
conditions. Here, we explore these implications. Further, we draw on
participant discussions during the workshop and field activities to
propose potential reasons for the differences in observations between
participants.
Scanline validity and appropriate data collection
method
As for all forms of sampling for data collection, scanlines must contain
enough data points to be statistically valid, and the required number of
data points depends on the investigated characteristic of the fracture network.
However, our data demonstrate that in addition to the fracture network
characteristics, the required scanline size (length of a linear scanline,
circumference of a circular scanline, or area of a window sample) is also
dependent on who is collecting the data.
Topological sampling results for individuals and groups for circular
scanlines 1, 3, 4, 5, and 8. Each histogram reports the results for all
workshop participants. The statistics have been derived from the data for
each participant. Data are presented as both bar charts and shaded histograms
with the bin width, b, indicated on the chart (please note the bin width
varies between circles as a function of the range in reported or calculated
values). In all cases the y axis represents frequency and is scaled so the
shape of the distributions can be assessed.
Different participants clearly observed different numbers of fractures in
the same scanline (Table 6, Fig. 2).
Zeeb et al. (2013) suggest that a
minimum of 225 fractures should be sampled for linear scanlines and 110 fractures
for window sampling. For Line 3 participants reported between 1.4 and 2.5
fractures per metre. If we apply Zeeb's recommendations, the cumulative
length of scanline for the person who reported a lower number of fractures
per metre would need to be nearly twice the length (160 m) of the
representative scanline for the person reporting higher fracture numbers
(90 m). The number of fractures in Circle 5 reported for window sampling
ranged from 13 to 56, which means between two and nine circles of this size would
need to be analysed to statistically represent the network. The variation
between how participants view the fractures therefore results in
significantly different lengths of scanline or numbers of circles to capture
a representative sample of that network. Our data show that there is not a
great degree of difference in the time taken by participants to characterise
the same fracture network, although there is different detail. However, the
simple fact that one geoscientist needs to find over 4 times more locations
to draw out circles of the same radius on a particular outcrop will likely
mean that collecting equivalent datasets may take longer for a less
detail-oriented participant. Where a detailed-orientated operator may fall
down, however, is when a fracture network displays a degree of heterogeneity
or clustering. In this case, although a detailed-orientated operator would
report the required number of fractures according to Zeeb et al. (2013), they may fail to cover enough
ground to understand the spatial distribution of fractures the way a less
detail-orientated operator would.
The effect of subject bias on the validity of circular scanlines.
The number of terminations recorded by individuals or groups is displayed for
each circle and colour-coded depending on where a valid (> 30,
green), possibly valid (20–30, yellow), or invalid (< 20, red) number
of terminations was recorded.
The appropriate radius of the sample window is also dependent on the sampling
behaviour of the operator. For circular scanlines it is widely agreed that a
minimum of 20–30 fracture terminations within a circle is appropriate to
derive fracture statistics or undertake topological sampling, and the circle
radius must be adjusted to capture enough fractures or fracture terminations
(Procter and Sanderson, 2017; Rohrbaugh et
al., 2002). Figure 10 shows the proportions of valid (capturing
> 30 terminations) and invalid (capturing < 20 terminations) results
for the circular scanlines in this study. Out of the 29 participants that
collected data from Circle 8 in the workshops, 12 identified over 30
fractures and so report valid results, another 8 collected over 20 fractures
and their results are potentially valid, whereas 9 reported fewer than
20 fractures, so the statistics derived from their sample may be
unrepresentative. Since the number of fractures identified in the field is
generally higher than in workshops, a greater proportion of field
participants reported sufficient terminations within the circle to be
statistically valid. For example, all field participants report valid data
for Circle 4, whereas only three of the nine groups in the workshops do.
In this work, the location and radius of all scanlines except C6 were
selected by Participant G/11, who tended to be more detailed than other
participants. This participant recorded enough terminations to class their
data as valid for all sampled circles. Therefore, this participant chose a
circle radius appropriate to the level of detail to which they identify and
characterise fractures, but which is not appropriate for other less detailed
observers. This effect is demonstrated in Fig. 11, which shows a synthetic
fracture set interpreted by an operator who gathered less detail-focused
observations (Fig. 11a) and an operator who gathered more detailed
information (Fig. 11b). A statistically valid circular scanline
(> 30 fracture terminations) is drawn onto the interpreted network, and the
resulting differences in the fracture topology and the fracture statistics
are shown (table in c). For this example, for the scanline to be
statistically valid, its radius must be 3 times larger for operator (a) than
operator (b).
The impact of interpreter style on fracture statistics of a
synthetic fracture network. (a) Statistically valid topological sampling
within a circular scanline for a fracture network which only considers the
large-scale fracture network. (b) Statistically valid topological sampling
within a circular scanline for the same large-scale fracture network as (a) but also capturing small-scale fractures at fracture intersections.
(c) The topology attributes (n, i, y, and x nodes), derived fracture
statistics,
and node triangle of the different interpretations of the fracture network.
How detailed a fracture network is interpreted to be by an operator
therefore affects the derived fracture statistics (Fig. 11c). The more
detail-focused interpretation (Fig. 11b) has more y nodes but similar counts
of n, i, and x nodes. As a result, the connectivity of the interpreted
network in part (b) is greater than that in part (a). The other fracture
statistics (intensity, density, and trace length) are very different between
different levels of interpretation detail. For example, the density of
fractures in part (b) is over 18 times larger than that of part (a), and mean
trace length is reduced from 1.71 m for part (a) to 0.47 m for part (b). This
variability is primarily due to the required circle radius, which is used to
calculate fracture statistics using circular scanlines (Table 1), changed in
order to capture the minimum number of fracture terminations. For our data,
if participants who recorded insufficient fracture terminations in their
samples (i.e. fewer than 20) to be considered statistically valid are
disqualified (i.e. removed from the dataset), the maximum trace length and
density are more affected by subjective bias than the fracture intensity and
connectivity. For example, the calculated maximum trace length for Circle 8
decreases from 2.88 to 0.92 m, and the maximum density for Circle 5
decreases from 46.5 to 12 f/m2.
Different fracture data collection methods are chosen depending on the aims
of the study, the way the fracture network is presented within the outcrop
(or core), and the homogeneity of the fracture network. Our data suggest that
window sampling is the least affected by subjective bias. In the process of
drawing out the fracture network, the operator is required to consider the
fracture geometries, evidence for fracture timing (e.g. cross-cutting
mineral fill types), and the implications of this for the fracture
statistics. There may be similarities with the findings of
Macrae et al. (2016), who showed in a randomised
controlled trial of industry experts that the quality of a seismic
interpretation could be increased by explicitly requesting
interpreters of seismic data to describe the temporal geologic evolution of
their interpretation.
Causes of subjective bias: operator bias and fracture network
characteristics
Human factors. We observe considerable variability between
participant interpretations, something which has also been observed by
Peacock et al. (2019) in the reported values of joint
intersections on a bedding plane. Additionally, our data show that individuals
display a degree of internal consistency (Figs. 3 and 4). That is,
individuals exhibited personal characteristics or traits through the data
that they gathered: they were either more detail-orientated or they were
less detail-orientated, allowing them to focus on gathering a larger
volume of data. We suggest that this reflects an operator's personal
approach to data collection: variability in data that are collected by a
single person is likely to be internally consistent from one data-gathering
exercise to the next. Care therefore needs to be taken when comparing
results from different operators. Our data show that it is important to consider
whether you are working with a “detailed” person who will likely wish to
include data on smaller or more detailed structures or if you are working with
a person who is more likely to focus on “the big picture” and to gather a
higher volume of data from a greater number of sample locations in the same
amount of time.
It is interesting to consider why people tend to be internally consistent in
their data-gathering approach, yet different from each other. It is likely
that they consciously or subconsciously construct their own protocols around
how the data should be collected and what features should or should not be
included. These protocols will be shaped by (a) practical and physical
factors such as the quality of an operator's eyesight, whether or not it is
easy for them to repeatedly crouch down to get a closer view and stand up to
move around, spatial coordination that affects the ease with which they
cover the scanline, and the time available to gather the data, as well as (b) inherent
cognitive or personality-related factors.
As an example, some participants focused only on more pronounced fractures,
ignoring, for example, smaller subsidiary fractures, closed or filled
fractures, or thin hairline fractures intersecting the scanline. This
behaviour was particularly common where a large or clear fracture is
present; the participant reports only the dominant feature. As one
participant exclaimed during group discussion “What do these tiny things matter – if you have a massive fracture?” However, this viewpoint
was not shared by all participants: others mentioned the importance of the
spatial distribution of small fractures either as indicators of strain
incompatibility or as the locus of flow at fracture intersections. It is
clear that decisions about “what feature counts” and whether a feature has
geological origins are subjective judgements.
Shipton et al. (2019) and
Gibson et al. (2016) discuss the concept of mental
models in the geosciences: a mental model is a simplified internal
representation of some external event or process. We suggest that our
participants' mental model of the processes that they are measuring may
guide their attention to particular features and obscure or censor the
network that they record. The mental model and therefore the features – or
scale of features – observed may also be influenced by the intended
application for collected data
(Shipton et al., 2019) or the
conceptual model that the participant is working from (Shipley and
Tikoff, 2016).
While one may expect mental models to be shaped by the experience levels of
operators, this is not observed in our dataset. Scheiber
et al. (2015) studied different participants' observations from a single
lidar dataset and found no correlation between experience and the reported
number of bedrock lineaments. Similar to our work,
Scheiber et al. (2015) found that participants who
reported the largest number of lineaments observed the greatest number of
small features, and these small features often did not follow the main
orientation trends seen in the data. Biological studies also find no
evidence for a relationship between level of experience and the detail of
observations (e.g.
Dickinson et al., 2010;
Dunham et al., 2004).
We suggest that the cognitive style of the participant is more important
than experience in how a participant interprets the studied media: the
fracture network. Cognitive style refers to how an individual perceives,
thinks, solves problems, learns, makes decisions, and interacts with others
(Witkin and Goodenough, 1977). The work of Jung (2016), particularly the use of the Myers–Briggs Type
Indicator (Myers, 1962) to assess cognitive style, underpins
much of this field. Jung's theory outlines three facets of cognitive style,
each with endmember preferences (Myers et al., 1998):
perception dictates whether a person is either meaning-oriented (intuitive) or detail-oriented
(sensory), judgement dictates whether a person makes decisions based on analytical and logical
means (thinking) or through a set of personal values (feeling), and environment dictates whether a person
reacts to immediate and objective conditions (extrovert) or by looking inward to
their internal and subjective reactions (introvert) when reacting to their
environment. On top of these three facets, people often have an innate
preference for either perception or judgement trains of thought such that a perception person
has a tendency to use sensing and intuition-orientated thought, while a
judgemental person uses a combination of thinking and feeling. It is well
known that cognitive style can have an impact on how people respond to
stimuli and make decisions (Jung, 2016). If a cognitive style
is at odds with the task at hand, for example when an intuitive participant
is required to undertake a detailed task which would be better suited to a
sensory participant, a lower performance is to be expected
(Chan, 1996). This has been reported in the case of
auditors (Fuller and Kaplan, 2004) and air
traffic controllers (Pounds and Bailey, 2001). A
“cogitative culture” is often observed in different professions and roles,
whereby people aim to fit their cogitative style to the task or workplace
environment (Armstrong et al.,
2012). A misfit between cogitative style and the task tends to be associated
with lower performance levels
(Chilton et al., 2005).
While cogitative styles may not be clear-cut (e.g.
Peterson et al., 2009), it is useful to adopt
endmember styles to consider how the cognitive style of the data collector
could, in theory, affect the fracture data they collect. For example, a
sensory participant should show high attention to detail, often observing small
fractures and subtle features of the fracture network that may tend to be
missed by intuitive participants. Conversely, while an intuitive participant may not
record small features, they should update their conceptual model more
frequently in response to new observations (e.g. a specific orientation of
fracture is consistently mineralised), leading them to develop a more robust
conceptual model of the subsurface (Shipley and Tikoff, 2016). A
thinking participant may collect more consistent or transparent data than
participants with other cogitative styles, for example by developing and
applying a set of logical and analytical rules.
The node data collected in our study are most consistently affected by
cognitive biases (Figs. 3, 4, and 6). Detailed-orientated participants
reported a greater number and percentage of y nodes compared to i and
x nodes. One of the underlying reasons for this was identified in the
workshop discussions, in which sensory-type participants described reporting
the small fractures at fracture intersections, whereas intuitive-type
participants reflected that they did not report these features, since they
believed (i.e. interpreted) that they would not contribute to flow.
Similarly, jogs in the fracture were classified systematically differently
by different participants; some considered jogs to be the termination
a fracture and initiation of another fracture, whereas others considered
jogs to be a slight sidestep of an otherwise continuous fracture. This
would have consistently affected the number of nodes reported.
Working in groups. We observed that behaviour varied considerably
between groups and that the behaviour of groups depended on the cogitative
styles of individuals within that group (pairs, in most cases). For example,
in one group a participant explained “[when we started working together] I very quickly … realised that [their partner] cares about
tiny features, so, together we cared about tiny features … but I was aware that if I was working on my own, I would have done it differently”.
This group evidently consisted of
participants with different levels of detail-orientated behaviour, and the
participant who individually displayed a less sensory cogitative style
tended towards the level of their partner. This is perhaps an example of
herding behaviour, often towards a more detailed
approach. Another participant reflected “I didn't find we [their group] were talking about `does this fracture count?'
Instead, we were discussing whether something was a Y–Y or an X, or where exactly a fracture goes or where it terminates and so on”.
This group appears to be made
up of two intuitive-type participants, who worked well together
discussing the meaning behind the fracture network.
The very knowledge that you are working together might be effective in
itself. As one participant articulated “the very knowledge that you are working with someone changes your approach.
You want to engage together and so you need to defend or explain your choice, which makes you more alert to what you are doing and why”.
This suggests that for fracture
analysis a group comprised of different cognitive styles could be
advantageous in terms of capturing the range of perspectives and potential
interpretation styles. Fracture network analysis is not simple; it requires
not only the identification of fracture traces, but also a consideration of
how these fracture traces from a network
(Peacock and Sanderson, 2018).
Parallels may be drawn to the findings of Cheng
et al. (2003), who found that when participants were asked to complete a
complex accounting task, groups comprised of different cogitative styles
outperformed homogenous groups. That said, working in mixed groups can be a
cause of conflict and introduce errors due to a negative effect on the
ability to reach a consensus in the decision-making process
(Aggarwal and Woolley, 2013). In our study, some
participants felt that working as a group slowed down the data collection
process to a problematic degree. However, this was only observed in WS1; the
sampling time was comparable for individuals and groups in WS2 (Fig. 3,
Table 5). Interestingly, there are many different interpretations of what
“working together” means and what shapes working together takes. While for
many, this meant working through the scanline together, others elected to
divvy up the window or scanline, working separately and combining their
results at the end, or for one person to be the data gatherer and the other
the data recorder (i.e. the scribe). For the latter two models of working,
the potential benefits of discursive or deliberative group work (i.e.
rationalising and laying bare thought processes) will not be leveraged.
Projecting into areas of limited exposure. The effect of subjective
bias on the required length of linear scanlines, the radius of circular
scanlines, and the area of sample windows will have particular consequences in
areas of limited exposure, and a detail-orientated operator may not be
able to collect enough data to statistically represent the fracture network.
In the discussions following WS1, several participants reflected that where
exposure was limited or obscured, they did not attempt to interpret where
the fracture went or the type of fracture intersection, since this was
straying too far from quantitative observation into more qualitative
interpretation. Other participants, however, did interpret the network
despite these difficulties, which increased the number of nodes that they
reported and decreased the number of illogical floating nodes. Clearly,
some felt it was most appropriate to interpret in the face of uncertainty
so as not to discount nodes that could be logically inferred, while others
felt that this would be over-interpretation. Both viewpoints have internally
consistent reasoning, but will produce very different outcomes in terms of
fracture network characteristics to be applied to analyses of fluid flow or
rock strength.
In some cases, these uncertainties could easily be overcome in the field:
for example, when a fracture was obscured by shadow or seaweed. Some field
participants described “feeling” for the trace of a fracture with fingers or
pencils when obscured (e.g. by seaweed) or difficult to see. Some also
describe inferring fracture trace by extrapolating from the exposed traces,
triangulated by observing the general fracture trends. Such “exposure bias”
is recognised when studying fault zones; by their nature, the fault
rocks are often preferentially obscured and therefore good continuous
exposures of fault zones are very rare
(Shipton et al., 2019).
The scale of observation. In the workshops, participants were
provided with a 2-D “bird's eye” view of the full circle being sampled. In the
field, only the tallest operator will be able to observe the full circle,
while all others would have a more limited field of view. In the field, the
participant can potentially crouch down and “get their eye in” to the detail
within a complex fracture network. The ability to adjust the scale of
observation during data collection in the field is most likely the reason
for more nodes reported in the field than in the workshop for the same
circular window (Fig. 10). Similarly, it is important that the same scale of
observation is maintained when using remote sensing methods. For example, it
is important that an operator does not zoom into areas of interest unless
they do so systematically.
Using preset data cut-offs. It is clear that a meaningful
quantitative analysis of fractures requires a certain degree of consistency.
This is particularly relevant for combining or comparing data collected by a
number of individuals, including for meta-analyses. Participants in WS1
discussed whether their collected data could be more readily compared or
combined if a minimum trace length cut-off was applied to the data. However,
no consensus could be reached about the scale or style of the cut-off to
be applied because (i) it would not be an accurate representation for flow
and/or rock strength, and (ii) more attention should not be paid to simpler,
larger, and more isolated structures that could have almost no flow or
mechanical significance. The use of size cut-offs has been used in scanline
studies which investigate aperture size distribution (e.g.
Hooker et al.,
2014; Ortega et al., 2006). Fracture trace length, however, differs from
aperture studies in that what you are measuring (the number of fractures) is
not a clearly defined parameter (i.e. aperture size) but instead highly
subjective. This stems from the fact that most opening mode fractures show
evidence of growth through the linkage of several smaller fractures, and,
due to the fractal nature of fractures, a single fracture tends to be
comprised of several smaller fractures (e.g. Bonnet et
al., 2001), so the fracture count is dependent on the scale of
observation. We observe a similar effect in our dataset, whereby participants
differ in their interpretations of where a fracture starts and ends and
whether fractures with jogs should be classified as one continuous feature
or multiple fractures.
Another knock-on effect of having no data cut-off is that the derived
statistics for fracture intensity or fracture density from reported data can return wildly differing results
(Ortega et al., 2006). From our findings, it is
clear people self-censor according to a minimum trace length, and this
minimum cut-off is variable in scale. That said, we find that the range in
reported values decreases towards 10 % to 15 % of the diameter of a circular
scanline or window sample. For example, for Circle 8 data (S8), the range
in the number of reported fractures is 36; however, when fractures < 5 cm trace length are removed the range falls to 19. The range stabilises if
only fractures > 10 cm in length are considered. This effect is
amplified for fracture density, which is calculated using the number of
reported fractures. The raw density statistics range by a factor of 3 (23 to
69f/m2); however, as you apply cut-offs to the data the values decrease and
converge so that when all fractures less than 10 cm in length are removed, the
difference between minimum and maximum values is reduced to 1.3 (18 to 25f/m2).
This suggests that it should be possible, depending on the aim of the study,
to apply a cut-off to the minimum trace length included in the dataset.
However, it is vital that this approach is reported; otherwise, the data
reported will not be replicable.
Recommendations for reducing subjective bias
We encourage reflective critique of the fracture data collection process,
including identification of potential uncertainties when collecting new
data and when collating or comparing fracture statistics from different
field studies. Drawing on our results, we propose the following approaches
to assess, reduce, and report the potential subjective bias in the data that
geoscientists collect.
(1) Understand your style of data collection. It is vital
that when collecting fracture data, either in the field or from photographs
(or e.g. remote sensing), that the “go to” style of data collection is
understood; i.e. detail-orientated vs. data-volume-orientated approaches. In relatively homogenous fracture networks a detailed
operator will characterise a network quicker as less circle is required
(i.e. detail-orientated will be preferable). In areas of regional
heterogeneity, however, it is better to include more circles covering
larger fractures (i.e. be more data-orientated). Finally, but most
importantly, it is vital that we report our own biases and methods used to
reduce bias in the field reports to enable replicability and comparison of
studies.
(2) Select your fracture data collection methods to limit subjective bias. While all methods of collecting fracture data are
susceptible to subjective bias, we find that window sampling is the least
affected. The approach does not take much longer than topology sampling (the
time taken is on par with topology sampling when working in groups and
< 1.6 times as long as topology sampling when working
individually). Thus, we recommend that, where possible, a window sampling
approach is adopted to collect fracture data. In addition, regardless of
which approach is adopted (circular, window, linear), the fracture network
should be traced out either on a printed photograph or tablet or with chalk on
the outcrop. Doing so for at least some of the sample windows would allow
participants to examine their own biases in how they classify fractures and
critique their collection approach. Since we find that the window radius, to
some extent, governs the size of the fractures observed and reported by
different individuals, we recommend that, if using circular scanlines, the
radius of the circle is kept the same across a sample area. However, we recognise that this could be problematic
in areas of drastically different fracture intensities where a “valid”
circle size for one sample location would not collect valid data at other
locations.
(3) Define what fracture features to include early on. Prior to the collection of field data, or as the first step
of field data collection, the sampling strategy should be reflected upon and
agreed, in line with the goals of the study and the characteristics of the
locality. For example, in fluid flow studies it is vital that information
for all connected fractures is included in the dataset, in which case the location
of small fractures that contribute to the network becomes key: simply
stating this may induce people to focus more on the small features (see
Macrae et al., 2016). The spatial distribution,
not just the relative percentage, of fracture terminations within a network
should be assessed and recorded when reporting fracture statistics. In the
case in which small fractures may be important, then it is important that all
the observed fractures are collected; however, subsets based on fracture
trace length should be used when comparing data. One could take the approach
that everything should be collected and only after collection should the
data potentially be censored for the purpose of further analysis (e.g. to
investigate the intensity of fractures above a certain trace length).
However, not every sampling campaign necessarily needs the same level of
detail, so adopting this approach could lead to the collection of a
large amount of unnecessary data as a function of campaign goals. If the
level of detail collected is superfluous to the needs of the study, the
overall data quality could suffer in terms of the extent of outcrop studied
(i.e. the number of detailed sample windows completed over a given area is
less than the number that would have been completed if the level of detail
relevant to the study was considered).
(4) Agree how to address data collection in areas of limited exposure. We recommend that operators take steps to ensure that the
fracture network they collect is complete (i.e. all node types have the
correct number of branches and the counts of parameters are checked) and
consistent with the network observed in areas of full exposure. This could
be achieved though the extrapolation of trends from outside the sample area
or through ensuring the consistency of the network within the sample area
(e.g. are E–W-trending joints consistently connected to N–S joints by
y nodes?). It is important that areas of no exposure (see Fig. 7d) are
interpreted as well as possible; otherwise, estimates of trace length and
connectivity will be unrepresentative of the network. This approach is also
important as it enables the operator to gain further insight into the
development of the fracture network: for example, a better understanding of
the age relationships between fracture traces (Procter and
Sanderson, 2017). If this is completed as the first step of fieldwork,
sources of counting errors can be identified and minimised. Regardless, the
sampling or counting error identified should be communicated as part of the
data reporting.
(5) Where possible, collecting fracture data from field exposures is preferable to interpreting field photographs. We find that
there is less variability in fracture data collected by different
participants when data are collected in the field rather than collected from
field photographs. Field-based observations have a number of advantages over
photo-based approaches: the operator can change position and distance for
more complex fractures, remove obstructing material, adjust so that
something is not in shadow, physically feel for the fracture, and check if a
feature rubs off or if it is continuous into another plane of the outcrop.
A further advantage of collecting data in the field is the ability to look
outside the sample area, to ensure that the fracture network within the
sample area is consistent with the wider network, and to enable kinematic
data to be collected. A caveat to this recommendation is that in the field
the quality of observations can be negatively affected by environmental
factors (e.g. rain, cold, heat, etc.) which are not encountered during
analysis undertaken in the office. Recording such factors and the likely
effect on one's field approach is good practice.
(6) Working as a group. Working as a group is preferable to
working individually to collect fracture data, since we find less
variability and fewer inconsistent nodes in data collected as groups.
However, group work should be considered a collaborative and dialogic
process, whereby participants discuss their rationale or reasoning before,
during, and after data gathering, as opposed to divvying up tasks to be
completed individually in a team. In the former, working together allows for
the identification and reconciliation of differences in interpretational
approach while improving the mechanics of the data gathering, thereby
reducing the potential for subjective bias by increasing the detail of
observations. The quality of the data collected will be more consistent as a
result. In line with this, a group comprised of different cognitive types is
preferable. In particular, sensory-type operators should be paired with
intuitive-type operators and encouraged to work collaboratively to tease
out whether and how the detail observed by a sensory participant is identified
and interpreted. The level of geological experience is not relevant to
consider when selecting groups, but the relationship dynamics within the
group should be managed such that the less experienced individuals feel
comfortable to actively discuss with those more experienced than them,
rather than simply consenting to their views or defer to their judgement.
If data are to be collected separately and then combined, then the sampling
behaviour of members of the team should be assessed prior to data collection
to establish if data from the individuals can indeed be meaningfully
combined. The sampling strategy should be conceived such that the minimum
number of moderate-scale obvious fractures should be captured (i.e. when
using a circular approach, the radius should capture 20–30 terminations of
the major fracture sets), with the small fractures still recorded. If
conducting collaborative fieldwork, for which operators are working individually
to collect data from different sampling sites, the team must first
characterise their own biases, then agree on a unified approach and
classification system, the process of determining sample location and
dimensions, and what to do when e.g. a particular fracture intersection is
obscured. It is important to characterise the way participants differentiate
fracture terminations and distribute reported trace lengths.
(7) Define a data cut-off. Because all fractures larger than
10 % to 15 % of the circle diameter are typically well defined by all data
gatherers, all data above this size can be confidently compared between
operators with different fracture judgements. The circle radius should be
set and reported prior to the start of the collection of field data. It
is vital that the scanline is large enough to cover enough fractures for the
least detailed-orientated member of a group to still collect a sufficient
number of fractures. We recommend that the scale of observation is kept
consistent throughout the survey and if a minimum fracture trace length
cut-off is chosen that it is clearly reported in field reports and
publications.
The procedure could be further improved and tested by either (a) using
a set of calibration scanlines prior to data collection to test personal
biases and familiarise the operator with the technique or (b) having a
scanline or sample area which is used as a marker and completed regularly
throughout the data collection procedure to test replicability, as also advised
by Peacock et al. (2019). While the above procedure
is undoubtedly helpful and goes some way to providing
consistency in fracture data collection, it also does not take into account
the fact that behaviours may change through time (e.g. Scheiber et
al., 2015). Such changes may be due to things such as experience with the
data-gathering procedure, experience with trends in the fracture network being
classified, subsequent training (e.g. the introduction of minimum trace
length cut-offs), or undertaking fracture data collection with differing
survey goals (e.g. paleo-stress analysis vs. fluid flow studied). Due to
this,
the procedure should be repeated regularly and assigned to “single events”
such as a day in the field or a single data collection session.
(8) Communicate the steps taken to manage bias in data collection. Steps one to seven should be communicated as part of data
reporting and publication.
Wider geoscientific implications
While this work concentrates on a field-based approach, which uses several
data points (sampling areas) to collect data from an outcrop, many of our
findings are also relevant to the collection of data from broadscale
approaches such as UAV- or remote-sensing-derived maps. With the advent of
digital-image analysis techniques and UAV technology, it can seem preferable
to perform digital fracture mapping; however, uncertainties regarding
hairline fractures, potential weathering features, or vegetation obscuring
the fracture network, for example, can be more easily explored by direct field
observations. One may expect marginal error, which is a function of
the sample size, to be reduced by digital fracture mapping, since digital
mapping allows a much larger number of (and area of) fractures to be
sampled in a given time. We instead suggest this not to be the case because
each participant is in effect using their own method to identify and
classify features on the digital image being studied, and many of the subjective
biases that we observed in our work will be applicable to remote mapping
methods. This corroborates work by Scheiber et al. (2015), who investigated the number of lineaments identified by six
participants interpreting the same lidar dataset (at the same resolution).
Extreme variability was observed between participants, who counted between
74 and 607 lineament traces (COV =1.61). Indeed, concern about
consistencies in image interpretation was raised in early work on remote
imagery; Huntington and Raiche (1978)
suggested that inter-operator variability in the interpretation of
lineaments from Landsat imagery could be so significant that it may seem as
if different scenes with different geologies had been interpreted.
In this work, we have demonstrated, for the first time, the clear need for
geoscientists to develop consistent and transparent protocols for collecting
field data that are scientifically rigorous. We find that the type and scale
of subjective biases that affect how we identify, classify, and report
fracture characteristics are independent of experience and appear to be
related to personal character traits. It is vital that the geoscientific
community becomes more aware of the potential for subjective bias, the
subsequent effect on scientific uncertainty, and options to manage biases.
Indeed, we feel that these issues should be discussed openly from the very
first time that students collect field data. Training schemes and procedures
should be developed that not only consider the relative differences between
methods (as in Watkins et al., 2015), but also
the inherent human factors which affect data collection. These schemes will
differ based on the specific aims of the study; however, approaches to
manage subjective uncertainty in data must be communicated openly to
enable the study's findings to be replicable and to facilitate comparison
with other field data.
In fact, we propose that a series of reasoned recommendations or protocols
derived from and adopted by the scientific community could prove valuable to
streamline the data collection process and reduce the uncertainty in
observation-based sciences. The recommendations for field-based fracture
data collection may be different to those for remote sensing images. Any
such workflow should not be so prescriptive as to be inhibitive or to limit
the scope of study; however, it should be supportive enough such that the
results obtained by the adopted method are replicable. Since the type and
scale of subjective bias are independent of the level of experience or
expertise, a suitable workflow should enable crowd sourcing or citizen
science to be a useful medium for fracture data collection and analysis in
such a way that is commonplace in ecological studies
(Dickinson et al., 2010). Indeed, our work has
implications beyond the geoscience discipline; for example, to garner
maximum potential from big data, these subjective uncertainties and any
protocols to manage them must be reported. However, our work also
demonstrates the clear need for further work in this field to test the
effects of subjective or operator bias on the collection of fracture data,
both in the field and using maps generated from remote sensing, in addition
to investigating the role of subjective bias in other forms of geological
data and beyond.
Conclusions
In Sir Arthur Conan Doyle's Silver Blaze (1892), Sherlock Homes states “I
only saw it because I was looking for it”. We observe that this behaviour
may be common in geoscience data collection and has the potential to impart
subjective biases to the data collected, introducing uncertainty in the
geological information derived from these data and potentially affecting the
ability to replicate studies. We demonstrate that geologists' own subjective
biases influence the data they collect, and, as a result, different
participants collect different fracture data from the same scanline or
sample area. This has a consequent effect on the fracture statistics that are
derived from these data and that are used to inform geological models.
Although we find that participants can collect a range of data, we observe
internal consistency in the classification and number of fractures
gathered by each participant. This consistency is not related to geological
expertise, experience, or the time taken to complete the scanline, so we
propose that the underlying control on the subjective bias relates to the
individual's personal characteristics (detailed vs. pragmatic) and also the
process that the data will inform (bulk fluid flow? Scale of relevant
observation?). Major fracture sets tend to be captured by all participants,
so the subjective bias mostly affects the smaller-scale fracture
features. We find that the effect of subjective bias on the fracture
statistics derived from the observed fracture attributes can be large and
that trace length and fracture density are the parameters most
susceptible to subjective bias.
The subjective biases in how features are identified, classified, and
reported have implications for how data should be collected and collated.
Firstly, for the characteristics of a fracture network to be statistically
valid, a circular scanline should aim to capture a minimum number of
fractures in its area, and the radius should be adjusted to ensure that these
conditions are met. However, to meet the necessary validity criteria,
individuals who pay particular attention to small features could potentially
use a circular scanline with much smaller radius (and can consequently
collect data from smaller outcrops) than individuals who tend to dismiss
small fractures. Secondly, by comparing fracture data collected in the field
and from field photographs, we find that if possible fracture data should be
collected in the field, where the type of connections present can be
examined in more detail.
Drawing on the quantitative and qualitative data in this study, we propose a
series of methods for managing subjective bias. As well as supporting
individuals in understanding – and therefore mitigating – their own biases, there are
other practical steps that can be taken. For example, we suggest that the
perceived fracture network should be drawn out, either onto printed field
photos or using a tablet computer, to minimise bias by prompting the
operator to consider and report the trace length distribution and network
topology. Doing so also records not just the number of terminations and
individual trace lengths, but also where in the scanline the values
are recorded, and it also makes clearer the rationale behind the interpreted
fractures. For similar reasons, we also propose that people should work
collaboratively in (small) groups when gathering fracture data,
preferably with people who have different personal characteristics to them.
A series of protocols could be developed to streamline fracture data
collection and reduce uncertainties introduced by subjective biases, but,
ultimately, the steps taken to manage bias in data collection should be
communicated as standard during data reporting and publication.
This study is the first to quantitatively illuminate and discuss the scale
and potential causes of subjective bias in the collection of geological
field data. As the implications of our findings have relevance for a range of
observation-based sciences beyond geoscience from digital mapping to big
data, our study is, ultimately, a call for further work in this area.
Data availability
The workshop documents and data collected as
part of this study are available
in the Supplement. The data and
associated meta-data, which underpin this research, are freely available
and may be found at 10.15129/d3b26853-7236-4066-846f-7a6abb8d91bf (Andrews et al., 2019).
The supplement related to this article is available online at: https://doi.org/10.5194/se-10-487-2019-supplement.
Author contributions
Initial discussions and planning of the paper were undertaken by all authors,
with BA and JRR designing the workshops. The paper was prepared by BJA and
JRR, with contributions from all authors.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “Understanding the unknowns: the impact of uncertainty in the geosciences”.
It is not associated with a conference.
Acknowledgements
This work was funded through Billy J. Andrews' PhD studentship, supported by
the following: the Environmental and Physical Sciences research council (EPSRC, award
number EP/L016680/1); the ENOS project (H2020-EU.3.3.2.3) to develop
competitive and environmentally safe technologies for CO2 capture,
transport, storage, and reuse (record number: 664337); UKCCSRC, funded by the
EPSRC (EP/K000446/1, EP/P026214/1) as part of the RCUK Energy Program; and
the University of Strathclyde Global Engagement Fund. The authors also wish
to thank all the participants who attended the workshops and took part in
the fieldwork as part of this study.
Review statement
This paper was edited by David Healy and reviewed by Roberto Emanuele Rizzo and William Dunne.
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