We present pre-stack depth-imaging results for a case study of 3D reflection
seismic exploration at the Blötberget iron oxide mining site belonging
to the Bergslagen mineral district in central Sweden. The goal of the study
is to directly image the ore-bearing horizons and to delineate their
possible depth extension below depths known from existing boreholes. For
this purpose, we applied a tailored pre-processing workflow and two
different seismic imaging approaches, Kirchhoff pre-stack depth migration
(KPSDM) and Fresnel volume migration (FVM). Both imaging techniques deliver
a well-resolved 3D image of the deposit and its host rock, where the FVM
image yields a significantly better image quality compared to the KPSDM
image. We were able to unravel distinct horizons, which are linked to known
mineralization and provide insights on their possible lateral and depth
extent. Comparison of the known mineralization with the final FVM reflection
volume suggests a good agreement of the position and the shape of the imaged
reflectors caused by the mineralization. Furthermore, the images show
additional reflectors below the mineralization and reflectors with opposite
dips. One of these reflectors is interpreted to be a fault intersecting the
mineralization, which can be traced to the surface and linked to a fault
trace in the geological map. The depth-imaging results can serve as the
basis for further investigations, drilling, and follow-up mine planning at
the Blötberget mining site..
Introduction
In the last few decades the need for raw materials has increased worldwide (e.g.,
Dubiński, 2013, and references therein; Paulick and Nurmi, 2018). This
increasing demand also accounts for the European Union (EU). However, in
contrast to this demand, current exploration and mining activities and the
development of new mineral resources is still on a low level. Several mines
were abandoned between the 1960s and 1980s as mining
in Europe was too expensive (e.g., Crowson, 1996; Berverksstatistik, 2013)
and global prices were constantly falling. In recent years, the EU has aimed
to reactivate activities related to the exploration and production of
critical minerals, with a special focus on the so-called critical raw
materials (e.g., Malehmir et al., 2012, 2020). In that
context, a reliable and cost-effective exploration of such minerals is an
important step in the early stage of the whole raw materials value chain.
Therefore, the EU has supported several projects that focus on the improvement
of this exploration stage, e.g., through the EU-funded Smart
Exploration™ project (Mahlemir et al., 2019), which had the primary goal of
creating and improving new approaches for mineral exploration using geophysical
methods.
Seismic methods play an important role in mineral exploration (Eaton et
al., 2003a, b). They have the potential to allow for a
high-resolution characterization of mineral deposits at depth.
Reflection seismic surveys in particular can yield a structural image of potential
deposits, their host rocks, and other geological structures related to the
understanding of their genesis, such as faults and fracture systems. However,
reflection seismic methods in mineral exploration are not yet as well
established as they are in hydrocarbon exploration (L'Heureux et al., 2005). Their
application is often challenged by the corresponding hard-rock environment
causing strong scattering of the seismic wavefield and by complex 3D
structures, since the geological units can show strongly varying strike and
dip directions that may intersect each other. Furthermore, the expectable
signal-to-noise ratio is rather low due to low impedance contrasts and
strong scattering attenuation. Additionally, typical land seismic issues,
such as irregular source and receiver spacing, often poor source and
receiver coupling, topographic effects, and strong near-surface velocity
gradients must be considered during seismic data processing.
Despite these challenges, reflection seismic imaging has started to gain increased
popularity for use in mineral exploration (Malehmir et al., 2012). Several studies
have shown the potential of 2D and 3D reflection seismic investigations for
such mineral exploration (e.g., Milkereit, et al., 1996; Urosevic et al.,
2012; Cheraghi et al., 2012; Malehmir et al., 2012, and references therein;
Bellefleur et al., 2015), but methodological improvements are still needed
on the seismic imaging side, especially in cases with complex subsurface
structures and irregular acquisition geometries, which are typical for
seismic surveys in populated areas due to environmental and accessibility
issues.
The work presented in this paper has been performed as part of the Smart
Exploration™ project and focuses on imaging mineral resources using
reflection seismic methods with a special focus on pre-stack depth-imaging
techniques. We showcase this approach for an investigation area located in
the Bergslagen mining district in central Sweden (Fig. 1). The deposit
itself consists mainly of magnetite and hematite, which occurs in 30–50 m
thick sheet-like bodies dipping towards the southeast to around 850 m depth
(Maries et al., 2017; Malehmir et al., 2017). The 2D reflection seismic profiles
had already been acquired during 2015 and 2016 and cross the known
mineralization perpendicular to its main strike direction. The combined
dataset was successfully processed using a standard time domain processing
and post-stack imaging workflow (Markovic et al., 2020), Kirchhoff
pre-stack depth migration (KPSDM), and advanced imaging approaches
based on KPSDM (Bräunig et al., 2019). The results of these 2D surveys
show a clear image of the expected mineralized bodies and their surrounding
structures at depth. The obtained seismic images show that the known
mineralization likely extends deeper than previously known from borehole
investigations. The images also show internal structures (e.g., faults
causing vertical offsets) within the lateral extent of the reflectors.
Furthermore, several reflectors with an opposite dip direction were mapped
in the reflection seismic images. In particular, one of these reflectors is
of greater interest, since it apparently marks the lowermost end of the
deposits. However, in order to reveal the true 3D structure and to better
evaluate the potential resources, a sparse 3D seismic survey was conducted
in April–May 2019. The results of a conventional post-stack time migration
workflow were shown in Malehmir et al. (2021). Here, we present the
corresponding results of a pre-stack depth-imaging workflow applied to the
same dataset in order to provide further support and improved images of the
subsurface, and we also show the potential of depth-imaging algorithms for
such a dataset and level of geological complexity.
Geological map and survey layout with source (red) and receiver
(blue) positions of the 3D survey and the local coordinate system used for
first-break travel time tomography and depth imaging (black box). Courtesy of
the Geological Survey of Sweden.
For the previously acquired 2D seismic data, Bräunig et al. (2019)
demonstrated a suitable imaging workflow with pre-stack depth migration as
the last step resulting in a final depth image. Furthermore, they showed
that the application of focusing pre-stack depth migration techniques, such
as Fresnel volume migration (FVM) (Lüth et al., 2005; Buske et al.,
2009), coherency migration (Hloušek et al., 2015a), or coherency-based
FVM (Hloušek et al., 2015b) can improve the resulting image of the
mineralization for the 2D dataset and therefore allows for a more detailed
interpretation compared to a simple KPSDM approach. Following these
promising results, we also applied the FVM approach to the new 3D dataset
and compare it to the result of a basic KPSDM. The migration is guided by a
careful pre-processing sequence, including static corrections, and by a
reasonable choice of a migration velocity model.
Geology
The 3D seismic data were acquired over the Blötberget iron oxide deposit
of the Ludvika mines in central Sweden (Fig. 1). The area belongs to the
Bergslagen mineral-endowed district, which hosts a significant amount of
iron oxide and sulfide deposits. It is historically well known because of
its importance in the historical mining industry (Stephens et al., 2009). In
Blötberget, the deposits were mined until 1979 down to a level of
280–360 m (Malehmir et al., 2021). Several historical and newly drilled
boreholes investigated the mineralization, mainly at depths between 300 and
600 m (Maries et al., 2017). Borehole logs have shown that the mean P-wave
bedrock velocity varies between 5500 and 6000 m s-1. The P-wave velocity of
the main mineralization is in the same range and shows only some small
outliers with slightly higher velocities (Fig. 2). Nevertheless, the main
mineralization can be expected to be reflective, since the density log shows
a strong increase in density for the mineralized zones, and hence a potential
increase in acoustic impedance from the host bedrock to the mineralization can be seen.
Downhole geophysical logs, theoretical reflection coefficients, and
synthetic seismograms from the host rock and the main mineralization (from
Maries et al., 2017).
The deposits are situated within volcano-sedimentary rocks of the
Paleoproterozoic age (1.85–1.8 Ga), which are typically overprinted by
various degrees of metamorphism. More than 40 % of the iron ore produced
are from apatite-rich iron oxide deposits (Allen et al., 1996; Magnusson 1970)
and are considered to have a magmatic–hydrothermal origin (Jonsson et al.,
2013). The Blötberget area in particular is known for its high-quality
iron oxide apatite-bearing mineralization. More than 50 % of the iron is
hosted in magnetite and sometimes hematite horizons. Hematite deposits are
less massive and their skarn host rock contains more quartz and feldspar.
The mineralized bodies are intersected by mafic dikes and subvolcanic
intrusions (Pertuz et al., 2021),
The typical sheet-like mineralization occurs at different levels. At
Blötberget, two dominant apatite-rich mineralized bodies dip to
the southeast at an angle of 45 ∘ down to a depth of 500 m (Fig. 3). Below that they continue with a slightly shallower dip down to a known
depth of 800–850 m (Malehmir et al., 2017). No borehole data below 800 m are
available, and information on the lateral extent is missing. Therefore,
interpretation using the newly acquired 3D seismic dataset proves the validity of the depth
extension of the mineralization conducted in the former seismic surveys and
focuses on the lateral extent of the mineralization, the
surrounding structures, and their further characterization.
Perspective 3D view of the known mineralization (red and blue
layers and bodies) in the Blötberget area, together with the source and
receiver positions (red and blue dots, respectively) of the 3D survey and a
digital elevation model showing the topography in the study area.
Seismic data acquisition
The seismic dataset for this study was acquired during spring 2019. Figure 1 shows the geometry of the seismic survey onto the geological map,
including all source and receiver locations in red and blue, respectively.
The black rectangle indicates the lateral extension and location of the
resulting 3D seismic cube described in detail below. Figure 3 shows a 3D
perspective view of the known mineralization (red and blue surfaces; their
model is based on former mining activities, borehole data, and the previously
acquired 2D seismic data) and the surface topography and the acquisition
geometry of the 3D survey.
The image cube (black rectangle in Fig. 1, white box in
Fig. 3) has a horizontal extension of 2.3 km × 4.1 km. Its longer axis is oriented in a NW–SE direction and follows the central
line of the 3D survey, which is identical to the previous 2D seismic survey
acquired in 2015 and 2016. Its shorter axis runs almost parallel to the
strike direction of the main geological units of interest. The vertical
extension of the cube in Fig. 3 is 2.25 km (from
-250 to 2000 m b.s.l.).
For the 3D survey a combination of cabled and wireless receivers was used
with 1266 receiver points in total. The 32 t vibroseis source of TU
Bergakademie Freiberg was used as seismic source with a linear up-sweep length of
20 s, a frequency bandwidth of 10–160 Hz, and vertical stacking of
three sweeps per source point. Overall, 1056 source points were acquired,
distributed mainly along existing forest roads in the area, resulting in
a rather irregular and sparse 3D geometry. The internal receiver spacing
along the lines was 10 m along three NE–SE-oriented lines and
20 m for all other lines. The northwestern part of the investigation area is
covered relatively well with source–receiver azimuths in all directions,
while the southeastern part contains only receiver points and no shot
points along the central line. The layout was chosen like that since the
mineral-deposit-related structures of interest strike from southwest
to northeast and dip to the southeast. As a consequence of this
survey layout, the near-surface part is covered and illuminated well in 3D
(see Fig. 5 in Mahlemir et al., 2021), while the deeper central parts are
presumably less well covered and illuminated. The layout of the survey was
caused by two restrictions related to environmental and logistical issues.
The first was the restriction imposed by the vibroseis truck on the
available roads, which was a problem in the southeastern part of the
central line where the truck could not enter due to weight limits on the
access roads. The second limitation was related to the usage of cabled
receivers and limited wireless recorders available for the survey. Moreover,
the majority of the used wireless receiver system required a communication
between single receivers in the field so that a linear setup was also
necessary for these receivers. A minor number (10 %) of receivers were
fully autonomous recording wireless stations, and these receivers were used
to cross the main road interrupting the profile in the southwestern part of
the layout and were distributed along the existing road that extends the
layout to the southeast. All acquisition parameters are summarized in
Table 1.
Acquisition parameters of the 3D reflection seismic survey.
Acquisition parameters Number of live channels1266 (fixed spread)Acquisition systemSercel 408 (cabled and wireless), Wireless Seismic RT2 (wireless)Sampling interval1 ms / 2 msReceiver spacing10 m / 20 m along linesReceiver typeVertical component geophones (4.5, 10 and 28 Hz)Number of source points1056 (along receiver lines)Source spacing10 m along receiver linesSource type32 t vibroseis truckSweep parameters10–160 Hz linear upsweep, 20 s sweep length, 60 % peak forceNumber of sweeps per source point3Depth imaging
The imaging workflow consists of three steps, which are described in detail
in the following sections. The first step is the signal pre-processing of
the data in the time domain. This step includes static corrections, which are
handled later. The second important part in our imaging workflow is the
creation of a macro-level velocity model that, together with the pre-processed
data, serves as an input for pre-stack depth migration, which is the final
step in the workflow.
Data pre-processing
Figure 4 shows an exemplary single shot gather
before and after pre-processing. In general, the dataset exhibits an
excellent quality with good signal-to-noise ratio for such a hard-rock
setting, with sharp first arrivals and several clear reflections already visible
in the raw shot gathers (Fig. 4a). The
dataset has been pre-processed following the processing flow listed in
Table 2. The focus in the signal-processing sequence
was on a consequent suppression of surface waves and the boosting of the coherency
of the reflected signals from the ore bodies and their surrounding
structures. The low-frequency surface waves (orange ellipses in Fig. 4)
were successfully suppressed, and the visible reflection signals are
enhanced. The latter appear clearer and more continuous along the single-receiver lines and are traceable throughout the whole shot gather (see
yellow arrows in Fig. 4b).
An example shot gather of (a) the raw data and (b) the data after
pre-processing as described in Table 2. The source
position of the shown shot gather is located close to receiver number 1250.
The yellow arrows mark some visible reflections interpreted as being caused by
the mineralization in the data; the orange ellipses exemplary mark surface
waves present in the raw data.
Pre-processing flow applied to the dataset. All steps up to
amplitude scaling are identical up to step 5 in the processing method given in Table 2 of
Malehmir et al. (2021).
Processing parameters 1. Sweep correlationUsing theoretical sweep2. Vertical stackingThree sweep records per source location3. Geometry setupFixed 3D, sparse4. Amplitude normalizationSurface-consistent amplitude for shots and receivers5. Minimum-phase conversionBased on matching filter using theoretical sweep6. Refraction staticsApply static correction based on refraction travel times and shift to final datum of250 m using 5000 m s-1 replacement velocity7. Automatic gain control (AGC)200 ms window length8. Spiking deconvolution80 ms operator length, single trace9. Bandpass filter15-35-145-165 Hz10. Surface wave attenuationWavelet-transform-based attenuation (v≤2700 m s-1)11. Frequency–space domain (FX) deconvolutionYes12. Amplitude scalingWhole-trace rms amplitude balancing13. Top mute30 ms below the picked first arrivalsRefraction statics
In order to adequately account for the influence of the near-surface
low-velocity weathering layer in combination with widely varying topography,
we use 3D refraction static solutions based on a 3D first-arrival
travel time inversion, followed by a shift to the final datum using a
constant replacement velocity, which is in the range of the expected surface
bedrock velocity in our investigation area. Static corrections are
reasonable in some cases as they basically remove the influence of the
complete near-surface weathering layer from the data. Since the velocities
below the weathering layer are expected to be slowly varying both laterally and
with depth, a simple macro velocity model can then be used in the next step
for migration.
The first arrivals were manually picked for the whole dataset and used to
calculate refraction statics using two methods available in the used
processing software (TomoPlus from Geotomo Inc.): (1) generalized refraction
travel time inversion (GLI3D, Hampson and Russell, 1984) and (2) first-arrival travel time tomography (FATT) (Zhang and Toksöz, 1998).
GLI3D is a very robust technique to invert refraction travel times using a
layer-based model. Velocities in layers can vary laterally, except the
shallowest one. Nevertheless, the thickness is allowed to vary also for
this, mostly thin, layer and thus resolve the near-surface layer sufficiently.
FATT can be used to derive static solution in form of so-called tomostatics
(e.g., Bräunig et al., 2019). This method can be advantageous over
layer-based inversion in the case of strong topography or a lack of clearly
defined refraction interfaces, e.g., in mountainous areas (Cyz and
Malinowski, 2013). On the other hand, there is an ambiguity in determination
of the intermediate datum in tomostatics, which can affect final statics
values. In the case of the hard-rock seismic setting, a simple two-layer
refraction solution is usually used to represent glacial sediments and the bedrock.
We tested both methods for the Ludvika data, using all the available picks
in the inversion. The GLI3D solution was based on a two-layer model. For the
residual static calculations, only offsets between 200 and 2000 m were
used. Looking at the common-receiver and common-shot stacks without a statics
application (Fig. 5a and d), it is clear that statics are a significant
issue in our data. Although the receiver and shot static values obtained
using both methods do not differ significantly, one can see that there is a
better alignment of the energy visible in the stacks produced with the
application of the GLI3D statics (Fig. 5c and f) compared to the
alignment in the stacks of tomostatics (Fig. 5b and e), especially for
the shot stack (e.g., in the vicinity of shot 800; see arrows in Fig. 5).
Therefore, our final choice was to apply the GLI3D-derived statics to the
data. This choice allowed us to avoid a potential problem related to the
fact that application of the residual part of the statics to the
data would be required in order to properly use tomostatics in the depth-imaging
workflow. Furthermore, we would need to set the migration travel time calculation
grid fine enough to be able to reproduce the long-wavelength part of the
tomostatics. This approach would have been computationally too expensive in
3D (Jones, 2018).
(a–c) Common-receiver and (d–f) common-shot stacks calculated for
the data after a simple linear-moveout (LMO) correction and with the
application of the tomostatics (b–e) and GLI3D statics (c–f).
Migration velocity model building
As an input for pre-stack depth migration techniques, a good macro velocity
model in depth is needed. However, creating such a reliable migration
velocity model can be a challenging task for hard-rock settings, since clear
reflections are often missing while required for picking velocities within
conventional velocity semblance analysis. What is also very special in such
hard-rock environments is the relatively homogeneous velocity distribution
within crystalline formations, combined with relatively small velocity
variations between different rock types and typically slightly increasing
velocities with depth. Velocities up to 6000 m s-1 often appear already at
shallow depths. In combination with an additional weathering layer in the
uppermost part, which is typically characterized by low velocities
(<2000 m s-1) and significant heterogeneity, a strong vertical
velocity gradient can often be observed in the shallowest part of the
subsurface. A high-resolution near-surface velocity model would be required
to accurately address this shallow strong velocity gradient (Jones, 2018).
This would lead to a densely sampled migration velocity model (and therefore
a high computational effort), while the velocities in the hard rock itself
vary only very smoothly.
Here, the inverted near-surface velocity model was only used to calculate
static corrections, in contrast to the imaging workflow described in
Bräunig et al. (2019) where the near-surface velocity model was also
used directly as part of the migration velocity model. Borehole
investigations (Maries et al., 2017) have shown that the bedrock velocities
are in the range of 5600 m s-1 down to the target depth. Bräunig et al. (2019) used a migration velocity analysis (MVA) approach to extend the
migration velocity model below the shallow tomographic model down to the
target depth, also including the borehole logging information from Maries et
al. (2017). As a constraint, common image gathers with the mineralization-related reflector as a key horizon were used to iteratively update and
improve the velocity model. Therefore, the derived velocity model can be
considered to be reliable down to the depth of the expected mineralization.
Here, we use the MVA-derived part of the migration velocity model, which is
basically a 1D gradient velocity model with slightly increasing velocities
with depth. At the top of the velocity model, we use the replacement
velocity, which was also used during static corrections, as a starting value
for the 1D gradient model. The velocity values and the corresponding depths
are summarized in Table 3. The values are linearly
interpolated between the depth intervals and are kept constant within the
depth intervals.
The 1D migration velocity model.
Depth (m b.s.l.)Velocity (m s-1)-250 to -2105000500 to 125056001500600020006500Pre-stack depth migration
The application of the pre-stack depth migration plays a major role as the
final step in our workflow. As for the 2D data, we initially applied KPSDM
(Schneider, 1978; Buske, 1999), resulting in a first 3D seismic depth
image of the investigation area. Subsequently, we applied FVM as an
extension of KPSDM that limits the migration operator to the Fresnel volume
around back-propagated rays and focuses the image on the physically
contributing part of the two-way travel time isochrone (Lüth et al.,
2005; Buske et al., 2009). FVM was applied successfully to prior hard-rock
reflection seismic data in 2D and 3D (e.g., Hloušek et al., 2015b; Riedel
et al., 2015; Hloušek and Buske, 2016; Jusri et al., 2018; Bräunig
et al., 2019), including mineral exploration (Heinonen et al., 2019; Singh
et al., 2019). A key point in 3D FVM is the 3D slowness estimation from the
recorded data. The slowness is estimated directly from the recorded
wavefield using a local slant stack method with the semblance (Neidell and
Taner, 1971) as a measurement of wavefield coherency. It can handle
arbitrarily distributed receivers to estimate the most probable direction
for the emergent wavefield (Hloušek and Buske, 2016). Hence, the
slowness estimation (and therefore FVM) is completely data driven and needs
no a priori information on strike and dip directions of the expected structures.
This ability to image arbitrary dips and strikes without a priori
information makes FVM extremely robust for imaging in hard-rock
environments, especially when the signal-to-noise ratio is low, the coverage
of the data is sparse and the impedance contrasts of the expected structures
are small, as shown in Heinonen et al. (2019). Therefore, we used FVM as the
preferred imaging technique for the 3D dataset here in this study.
The pre-stack depth migrations (KPSDM, FVM) were applied to each shot
separately. The migration is performed on a uniform grid with a spacing of
10 m in each direction. The result is a 3D image for each shot gather, and these
are finally stacked to form the complete image (Buske, 1999).
As a first step, a constant migration velocity of 5600 m s-1 as a
representative value for the bedrock in the investigation area was chosen
for KPSDM in order to get an overview about the main structures and an
impression of the reliability and robustness of the applied pre-stack depth-migrated approach. Figure 6a and b show vertical
depth slices of the resulting image cube along the northeast–southwest
direction through the central part of the investigation area. Figure 6a shows the
plain image with two marked reflectors. The yellow arrows mark the expected
main mineralization reflector, which is dipping to the southeast. At its
lower end, this reflector is bounded by a crosscutting reflector (blue
arrows), which is dipping into the opposite direction. This crosscutting
reflector was also present in the result of the earlier 2D survey
(Bräunig et al., 2019; Markovic et al., 2020), but here this reflector
appears much clearer and sharper. Several other reflectors can already be found
in this 3D KPSDM image, which will be described in detail using the
FVM image below. Here, we concentrate on the two mentioned reflectors for
the evaluation of the imaging techniques and the used velocity model. The
dip direction and dip angle are well visible in the seismic image. However,
a detailed comparison of the image and the modeled mineralization (Fig. 6e) shows that the reflector is imaged around 50 m below the known model
layers. The reason for this mismatch is due to the constant velocity of 5600 m s-1 used for the migration, which appears to be too high. Choosing
iteratively different constant velocities for migration to find a
representative effective medium velocity could improve the tie between the
depths of the imaged reflector and the corresponding mineralization. Such an
approach would be comparable using different constant velocities for a
time-to-depth conversion for time domain imaging techniques. However, such a
calculation of an average medium velocity will not necessarily result in a
robust migration velocity model for all depths and would thus not be the
best choice to improve depth positioning and the image quality along
all reflectors throughout the whole 3D model. Therefore, we omit this
iterative improvement and instead concentrate on a more reliable 3D migration
velocity model in the next step. Before using this, we wanted to improve the
seismic image and therefore applied the focusing 3D FVM approach using the
constant migration velocity of 5600 m s-1. Figure 6c, d, and f show the same
vertical depth slices as in Fig. 6a, b, and e but here for the FVM image cube. The
arrows mark the same reflectors as in the KPSDM image: yellow arrows for the
main mineralization and blue arrows for the crosscutting reflector. When
comparing the KPSDM and FVM images (Fig. 6a and c, respectively) many
similarities but also several significant differences can be observed. Since
the used migration velocity model and the basic imaging technique are
identical, the imaged structures appear at the same position and depth.
Furthermore, all observable structures in the FVM image are already part of
the KPSDM image, but they are partly covered by incoherent noise and
migration artifacts in the KPSDM image. In general, the FVM image appears
much clearer than the KPSDM image. This is caused by the restriction to the
Fresnel zone along the corresponding travel time isochrones during FVM.
In addition, incoherent noise is reduced in the whole FVM image and the
coherent reflectors are more outstanding.
Depth slices through the KPSDM result(a, b, and e) and the FVM result (c,
d, and f) using a constant velocity of 5600 m s-1 for
migration, shown in (a) and (c) without and in (b) and (d) with the known mineralization
layers in red and blue. The yellow and blue arrows in (a) and (c) mark the
image of the main mineralization reflector and a crosscutting reflector
dipping in the opposite direction. Panels (e) and (f) are a 2D view
of the results for KPSDM and FVM, respectively. The gain for plotting was
chosen such that the reflectors of the main mineralization appear in the
same intensity for both techniques (KPSDM and FVM).
To evaluate the quality of both migration results, we try to estimate the
signal-to-noise ratio for both image cubes from KPSDM and FVM. Therefore, we
normalize both volumes to the root mean square (rms) of all amplitudes in
the volume so that the variability of the amplitudes is in the same range.
In a second step we calculate the median amplitude for all images and set
this median in relation to the rms value, assuming that the median amplitude
value is representative for the noise present in the image cube. The ratio
of these two values can be seen as improved signal-to-noise ratio and yields
a roughly 7 times higher signal-to-noise ratio of the FVM image in
comparison to the KPSDM image. Due to the improved signal-to-noise ratio,
the crosscutting reflector appears more continuous. Its shallow
part in the southeast is especially visible in the FVM image (upper blue arrow in
Fig. 6c), while it is covered by incoherent noise in the KPSDM image
(Fig. 6a). Overall, the imaged reflectors are more continuous and easier
to identify in the FVM result.
The same depth slices as in Fig. 6 shown through the FVM result using the 1D migration
velocity model and including information from MVA (Table 1) presented (a) without and (b) with the known mineralization in red and blue. The
arrows in (a) mark the image of the main mineralization reflector and a
crosscutting reflector dipping in the opposite direction (compare
Fig. 6c and d). Panel (c) shows a frontal view of the
important portion of (b) together with the main mineralization in red.
As the next step, the constant migration velocity model was replaced by the
1D gradient model described in Table 3. Figure 7
shows the FVM result using this 1D gradient model for slowness calculation,
ray-tracing within FVM, and travel time calculation. The shown slice is
located at the same position as the slices for the KPSDM image and the FVM
image using a constant migration velocity (Fig. 6). Here, the same main structures can be identified. The reflector related
to the main mineralization is marked again with yellow arrows. Compared to
the previous results it is imaged slightly shallower but with approximately
the same dip angle. The reflector itself is more coherent than in the case
of a constant migration velocity (Fig. 6) and the
image of the reflector appears straighter in its shape. The crosscutting
reflector, marked with blue arrows in Fig. 7a and c, is also imaged at shallower depths. In contrast to the main
mineralization, the dip of the crosscutting reflector appears steeper when
using the 1D gradient velocity model instead of the constant velocity for
migration. Furthermore, the image of the reflector is more coherent and
exhibits a higher amplitude. Figure 7b and c shows
the FVM image based on the 1D gradient together with the known
mineralization (blue and red bodies). The image of the reflector coincides
precisely with the depth position and dip of the known mineralization. At
the lower end of the model, the imaged reflector continues down to greater
depth and further to the southeast, where it ends at the crosscutting
reflector.
Figure 8 shows a selection of other vertical depth
slices through the FVM image cube based on the result using the 1D gradient
velocity model. The slices are all oriented from northwest to southeast and
have a spacing of 100–200 m. The location of each slice in the local
coordinate system is indicated in the upper right corner of each subfigure.
The slice in Fig. 8a, located in the northeast of
the investigation area, shows a prominent reflector marked with M1, and this
reflector can directly be correlated to the upper main mineralization (red
layer in Fig. 3). In this slice, the image of the reflector appears
relatively curved and interrupted in the middle part. The curvature can be
explained by the image being affected by migration artifacts (migration smiles) due to
the fact that the slice is located at the boundary of the investigation area
and therefore being insufficiently illuminated. This could also be the reason
for the interruption in the middle part of the reflector. Below the main
reflector M1, several other low-amplitude reflectors can be identified. In
the second slice (Fig. 8b), the reflectors are
better illuminated. Now, the reflector M1 appears as a high-amplitude
coherent reflector with only a slight curvature at the upper northwestern
end. It dips about 30∘ to the southeast and is imaged between 240 and 840 m depth. The dip angle and dip direction are in good agreement
with the dip of 25 to 30∘ in the time-migrated and
depth-converted image of Malehmir et al. (2021). The reflectivity below the
M1 reflector in Fig. 8b appears more coherent than
in Fig. 8a and distinct reflectors can be
identified (green arrow). Furthermore, the previously described crosscutting
reflector (compare with Fig. 7) is well visible
(C1, blue arrow in Fig. 8b). It dips with an angle
of approximately 25∘ to the northwest and is imaged between 400 and 740 m depth. The reflectors M1 and C1 are intersecting at 725 m depth,
where the C1 reflector marks the lower end of the coherent and straight
image of the M1 reflector.
Depth slices through the final FVM result based on the 1D
migration velocity model. The sections in (a) to (h) are spaced by 100–200 m
in the y direction. Several reflectors are named and marked with arrows: yellow
marked reflectors correspond to the known mineralization, reflectors marked
with green arrows are located subparallel below the known mineralization, and
blue arrows indicate reflectors dipping into the opposite direction of the
known mineralization.
Beside the C1 reflector, a second weaker reflector is visible at shallower
depth (blue arrow). It dips about 20∘ to the southeast and is
imaged between 160 and 360 m depth. This reflector is traceable only over
some slices. In Fig. 8c, it is almost not visible
anymore. The M1 reflector appears again as a sharp and strong reflector. The
dip is still around 30∘, but the reflector can be traced between
35 and 830 m depth with a spatial extent of approximately 1700 m in this
slice. It is again crossed at its lower end at 780 m depth by the C1
reflector. The latter is imaged slightly deeper than in the previous slice
and shows approximately the same dip angle of about 25∘. This
changing depth suggests a 3D orientation of this reflector, which is not
perpendicular to the slices selected here. Since the imaged depth is
increasing, the true strike direction is oriented north–south. However, it
is imaged clearly between 250 and 990 m depth. Above the intersection with
M1, it is imaged as one continuous reflector, while it appears more
disrupted below the intersection, where it also intersects other coherent
reflectors that are oriented subparallel to the M1 reflector (green arrow).
This reflector is imaged between 500 and 1000 m depth and is dipping with
almost the same angle as reflector M1 to the southeast. The lower end of
this reflector is also marked by the crosscutting C1 reflector. Besides
these main reflectors, some deeper and less strong and coherent reflectors
can also be observed. They are all dipping to the southeast and with an angle
around 30∘, comparable to the M1 reflector.
In the following slice Fig. 8d, the overall
structures are imaged in a similar pattern. The depth of the C1 reflector is
slightly increasing, the reflector appears less continuous than before and
shows a small offset at 480 m depth. The M1 reflector also appears less
continuous, and it is less sharp and coherent, especially in the upper part. The
deeper subparallel reflector also appears less coherent and continuous
together with a broadened signature, which also accounts for a more complex
3D structure for the M1 reflector and the underlying reflectivity. This
impression is confirmed by the image in the next slice at y=1100 m
(Fig. 8e). There, the reflector M1 can still be
identified but is also intersected by a second, slightly deeper reflector
with the same dip direction (M2). In addition, the underlying subparallel
reflectivity appears even more complex and less distinct than before. All
reflectors dipping to the southeast are confined by
the crosscutting C1 reflector at their lower end. The image again becomes
clearer in the next slice (Fig. 8f), where the M2
reflector becomes the most prominent and coherent reflector. It is imaged
between 190 and 770 m depth and dips with an angle of about 30∘ (the same dip as M1 reflector) to the southeast. The M1 reflector can be
identified only in deeper parts between 550 and 780 m depth with a
slightly steeper angle than the M2 reflector. Below these two reflectors,
some parallel reflectors are again visible with approximately the same dip
direction (marked with two green arrows). The C1 reflector is still visible,
although the signal is weaker compared to the previous slices. Here, several
other reflectors with a comparable dip direction are present and marked with
C0, C2, and C3. These reflectors exhibit a shorter spatial extent compared to
the others and are traceable only over some adjacent slices. Reflectors C2
and C3 can also be found in the next slice (Fig. 8g). They appear approximately at the same location, whereas the C1 reflector
is not visible anymore. The same applies for the M1 reflector which is no
longer distinguishable from the M2 reflector. The latter is imaged between
180 and 880 m depth; the underlying parallel reflectors (green arrows) are
still visible but are less distinct than before. The imaged reflector in this
area is more diffuse. Although reflector C1 is not directly visible, the
reflectivity of the M2 reflector and the underlying reflectors end along a
line that has the same dip as the C1 reflector before. The reflectivity in
the last slice (Fig. 8h) is again more coherent.
The M2 reflector is imaged sharper and is also steeper (35∘) than in
Fig. 8b to g. The lower end is confined by an almost horizontal reflector
(H1). The reflector C3 is still visible and shows a slightly listric shape.
The underlying reflectivity (green arrows) is still present in this image.
The visibility of the important structures in the seismic volume can be
summarized as follows: the M1 and M2 reflectors can be traced over all shown
slices. Since they are crossing each other and intersecting in some slices,
it is not always possible to distinguish between both reflectors. In all
shown slices, an underlying reflectivity can be observed. It consists of
partly distinct reflectors that are dipping in the same direction and with
a comparable dip angle to that of the reflectors M1 and M2. The lower end of
this reflectivity and the reflectors M1 and M2 is confined by the crossing
C1 reflector, which has an apparent dip in the opposite direction. Since the
imaged depth is increasing for slices to the southwest, the strike direction
of this reflector appears more towards the north–south rather than the
northeast–southwest direction. This orientation also explains why this
reflector vanishes in the slices in the southwest because it is presumably
not illuminated by the combination of sources and receivers anymore.
However, the reflectivity of the M1 and M2 reflectors, as well as the
reflectivity of the underlying reflectors, still ends at a line, which could
be an indirect hint toward a lateral continuation of the C1 structure. A more
detailed geological interpretation in relation to the known structures is
given in the following section.
Interpretation and discussion
The main mineralization, including its surrounding host rock structures like
the major crosscutting fault, are successfully imaged, which is the basis
for further structural interpretation. The reflectors related to the
mineralization are clear, pronounced, and with high amplitudes. They are
partly intersecting with varying characteristics in lateral direction, and
in some parts they exhibit a rather complex 3D shape. In order to constrain
the validity of the image, a detailed comparison of the imaged structures
with the geological model of the known mineralization assessed in a detailed
view on the FVM image, together with the current model of the second known
mineralization (Fig. 9, blue body, M2). The imaged position, the dip, and
the general shape of the reflectors fit almost perfectly to the
corresponding position of the known geological model of the ore bodies.
Furthermore, the reflector corresponding to the main mineralization (blue
body in Fig. 9) is traceable at least 300 m
further downward from the known downdip end of the mineralization.
Additionally, the seismic image reveals a bowl-type shape (likely a tight
fold) of this reflector in crossline direction (parallel to the main strike
direction), which can be followed laterally even further upward beyond the
known model of the mineralization (yellow ellipse in
Fig. 9a).
Perspective view (a) without and (b) with the model of the known
mineralization (blue body). The zoomed inset shows the good agreement of the
position, depth, and shape of the known mineralization and the corresponding
reflectors in the seismic image.
We tried to trace all imaged reflectors in the 3D FVM image cube and
manually picked the horizons to verify, complement, and extend the known
structural model of the mineralization and its host rock structures. The
reflectors were picked only when they showed coherent and strong amplitude
over a certain distance and were clearly traceable within the 3D seismic
image cube. Indirect structural indicators like phase offsets along the
reflectors or positions where reflectors seemed to be truncated were not
picked. Furthermore, partly reflective structures were not automatically
connected, but they were instead left as separate surfaces so that the
interpretation of their possible connection was left as objective as
possible. The picked horizons are shown in Fig. 10. Figure 10a and b represent perspective views
on the models of the known mineralization together with the picked horizons.
The view direction is from south to north (Fig. 10a) and from east to west (Fig. 10b),
respectively. The picked horizons M1 and M2 are shown in red and blue in
accordance to the known mineralization bodies, and the crosscutting
reflector C1 is shown in gold. For the picked C1 reflector, the
corresponding horizon extends downward to its lower end at a depth of
approximately 1000 m. It is illuminated by the source–receiver geometry
mainly in the central part of the investigation area. It presumably
continues further up the dip (to the southwest), but with the given acquisition
geometry it is not possible to illuminate it further towards the surface.
The same applies to the lower end of this reflector. It is possible that the
structure may continue deeper, but it is not illuminated by the acquisition
geometry. However, an extrapolation of this horizon (in form of a mean plane
for all picks, purple plane in Fig. 10c) shows its
possible continuation within the image cube. The surface outcrop of this
extrapolated horizon would be located in the western part of the image cube
with an almost north–south strike direction. Figure 10d shows where the mean plane would reach the surface and its relation to
the known mapped surface geology. The surface location and strike direction
of the mean plane fits perfectly to a mapped lineament in the geological map
(yellow arrows in Fig. 10d). Therefore, it is
highly likely that this mapped fault and the imaged reflector refer to the
same structure.
Interpretation of the identified and picked horizons. Panels (a) and (b) show perspective views on the picked horizons and the model of the known
mineralization. The crosscutting reflector C1 is extrapolated using a mean
plane (purple plane c), which intersects with the surface at a mapped
fault line (yellow arrows in d). The view in (d) is identical to (c) but
includes a map of the surface geology (courtesy of the Geological Survey of
Sweden).
As described above, the two main reflectors M1 and M2 show the same
location, dip angle, and shape as the known mineralization. In addition to this
agreement, the reflectors show an about 300 m lateral and downward
continuation of the previously known mineralization, meaning a potential
continuation of the mineralized bodies and therefore additional resources.
Assuming that the crossing reflector C1 is marking the lower end of the
mineralization bodies would allow us to fill the gap between C1 and M2, which
means an even larger downward continuation for this reflector, which is
directly visible in the FVM image.
Some of the reflectors can be directly related to the geology. M1 and M2 can
clearly be interpreted as reflected signals from the boundary between the
host rock and the main mineralization, which is known to be characterized by
a relatively high impedance contrast. Both reflectors show a bowl shape of
the mineralization bodies, and they are partly intersecting each other. The
imaged reflectors also indicate a potential greater lateral extension of the
mineralization.
The underlaying reflectivity (green arrows, M3) is only partly coherent and
shows a shorter lateral extension, but as the strike and dip directions are
identical to the overlaying mineralization, we interpret these reflectors as
also being mineralization related, meaning that there are potential additional resources for this
deposit.
Since C1 can be linked to a surface trace of a fault, it can be interpreted
as such. The weaker and shorter C0 reflector can also be interpreted as a
fault or as the contact zone between intrusive and volcanic rocks (see also
Fig. 1). The other imaged reflectors (C2, C3 and H1) are imaged only at
greater depth, and thus no direct link to the surface geology is possible. A
remarkable fact for these reflectors is that they are imaged in the vicinity
of the lower end of the imaged mineralization. They could be related to the
formation of the whole deposit.
Characteristics and interpretation of the imaged reflectors.
ReflectorStrike directionDip (∘)InterpretationM1SW–NE25–30Main mineralizationM2SW–NE25–30Main mineralizationM3SW–NE25–30Potential zones of mineralizationC0SW–NE20Fault (uncertain)C1SSW–NNE25FaultC2SW–NE25?C3SW–NE20?H1SW–NE5?
A comparison to the post-stack time-migrated and time-to-depth-converted
result by Malehmir et al. (2021) shows many similarities but also some
differences. The main imaged reflectors (M1, M2 and C1, or F1 in Malehmir et
al., 2021) are present in both results. The C1 reflector is much better
imaged in the pre-stack depth image from FVM. Here it appears as a
relatively sharp and continuous reflector, especially in the direct vicinity
of the lower end of the M1 and M2 reflectors. In contrast, the post-stack
time migration image of this reflector is only piece-wise evident and less
continuous, but it is also imaged in shallower depths. The reason for the
better image here is presumably the opposite dip of the intersecting
reflectors (C1 and M1, M2), which have to be adequately addressed during
stacking in the post-stack approach and might have caused some problems.
In contrast, the different dip directions are naturally handled by the pre-stack
depth-migration approach, and as such the reflectors are imaged properly.
Furthermore, the sharp image of the FVM allows for a detailed interpretation
of the visible reflectivity and for tracing individual reflectors through the
migrated volume. Thus, we were able to map the M1 and M2 reflectors,
resulting in the fold shape seen in Fig. 9.
Finally, the application of pre-stack depth-migration directly results in a
depth image rather than a time image. For the latter, a post-migration,
time-to-depth conversion is needed in order to interpret the seismic image
in depth. This conversion is done often with a velocity or a velocity–depth
function to best fit a priori (e.g., borehole) information. The pre-stack
depth image shown here is completely data driven and nevertheless fits well
with the a priori information. This means that a high reliability of the
resulting seismic image and especially the imaged depths and dips of the
visible reflectors can be assumed. The used imaging techniques, in
conjunction with a careful pre-processing of the data, are applicable
and well tailored for mineral exploration in hard-rock environments. Any
a priori information can be used for further optimization and validation. In that
sense, such imaging approaches are also interesting for the exploration of
less well-known or explored potential mineral deposits.
Potential avenues for future work include the incorporation of a more detailed P-wave
velocity model derived from, e.g., full waveform inversion (Singh et al.,
2021) for static corrections or even directly as part of a 3D migration
velocity model. Furthermore, the acquired 3D dataset could be used for a 3D
MVA using focusing pre-stack depth migration techniques to generate common
offset images, which then can be sorted into common image gathers depending
on not only the offset but also the angles of illumination. Since the
imaged structures are characterized by different strike directions and
inclination angles, together with conflicting dip situations, more advanced
investigations could be helpful. The already performed slowness calculation,
which is needed for back-propagating the rays within FVM, could be used to
distinguish between different emergent angles and directions for the
reflected signals within the application of focusing 3D pre-stack depth
migration variants. In order to improve the reliability of the imaged
structures and their shape, a proper illumination study should be considered.
This would also help to decide if the imaged reflectors ending at depth are
really ending where they appear to or if they are simply not illuminated.
Furthermore, the obtained structures are currently analyzed and interpreted
together with other geophysical findings and geological data in order to
obtain a comprehensive 3D model of the mineral deposit. The latter can then
serve as a reliable basis for prospective modeling and estimates of
its economic potential.
Conclusions
The acquired sparse 3D dataset provides an excellent basis for the
application of seismic processing and imaging techniques in the framework of
mineral exploration in hard-rock geological settings. Our workflow includes
the application of a tailored pre-processing flow, as well as the
application of a 3D Fresnel volume migration depth-imaging technique. Both
steps are accompanied by 3D first-break travel time inversion to obtain
static corrections within the processing flow, instead of handling static
shifts through the detailed velocity model incorporated in travel time
calculation, which would not be practical, as it requires very fine model
discretization. The application of static corrections allows for the use of a
simple 1D gradient velocity for the migration. It results in a sharp image
of the subsurface structures with a rather high accuracy in depth
positioning and allows for a detailed interpretation. Nevertheless, all
reflectors were also imaged using a constant migration velocity model, but
they appear with a less accurate dip and depth in the 3D volume.
The chosen processing approach delivered a high-quality 3D seismic cube with
several distinct structural features that could be correlated to the known
mineralization and also provide information of its possible extension in
lateral direction as well as towards greater depths. The study has shown
that reflection seismic methods and depth-imaging algorithms can also deliver a
high-resolution 3D seismic image for sparse and irregular acquisition
geometry.
Data availability
The presented data are available by contacting project coordinator Alireza
Malehmir or the corresponding author.
Author contributions
AM and PM designed the survey. AM, MaM, LB, SB, LS, EB, and FH contributed to
the data acquisition. MiM performed the signal processing and calculated the
static corrections. FH wrote the main content of the manuscript, applied
KPSDM and FVM, and created the 3D interpretation of the seismic data. All
authors contributed to the interpretation and discussion of the results and
to discussions during the processing of the data.
Competing interests
At least one of the (co-)authors is a member of the editorial board of Solid Earth. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Special issue statement
This article is part of the special issue “State of the art in mineral exploration”. It is a result of the EGU General Assembly 2020, 3–8 May 2020.
Acknowledgements
We thank all colleagues, students and young professionals involved in the
project Smart Exploration. A special thanks to all people involved in the
fieldwork of the 2019 survey and NIO for their support for planning and
during the field campaign. The GOCAD Consortium and Paradigm are thanked for
providing an academic license for GOCAD for 3D visualization and
interpretation of the data. We acknowledge the usage of the vibroseis truck
of the Technische Universität (TU) Bergakademie Freiberg, operated by the
Institute of Geophysics and Geoinformatics and funded by the Deutsche
Forschungsgemeinschaft (DFG) under grant no. INST 267/127-1 FUGG, which was
used as the seismic source in this survey. We also gratefully acknowledge
the Halliburton Software Grant to the Technical University Bergakademie
Freiberg, which enabled part of the data processing with their software
package SeisSpace/ProMAX. The depth migrations were calculated with the help
of the HPC cluster at TU Bergakademie Freiberg (DFG-grant INST 267/159–1
FUGG). Th Institute of Geophysics, Polish Academy of Sciences, acknowledges the use of the Globe Claritas seismic processing
package under the academic license from Petrosys Ltd and the TomoPlus
software (Geotomo Inc.). We thank Juan Alcalde, Isabelle Lecomte, two
anonymous reviewers, and the editors Liam Bullock and Susanne Buiter for
their helpful comments and suggestions on the original and revised version of
this article.
Financial support
This research has been supported by the Horizon 2020 Research and Innovation Programme (Smart Exploration, grant no. 775971).
Review statement
This paper was edited by Susanne Buiter and reviewed by Juan Alcalde and three anonymous referees.
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