In this study, we present a high-resolution dataset of
seismicity framing the stimulation campaign of a 6.1 km deep enhanced
geothermal system (EGS) in the Helsinki suburban area and discuss the complexity
of fracture network development. Within the St1 Deep Heat project, 18 160 m3 of water was injected over 49 d in summer 2018. The seismicity
was monitored by a seismic network of near-surface borehole sensors framing
the EGS site in combination with a multi-level geophone array located at
≥ 2 km of depth. We expand the original catalog of Kwiatek et al. (2019),
including detected seismic events and earthquakes that occurred 2 months
after the end of injection, totaling 61 163 events. We relocated events
of the catalog with moment magnitudes between Mw-0.5 and Mw 1.9
using the double-difference technique and a new velocity model derived from
a post-stimulation vertical seismic profiling (VSP) campaign. The analysis of the
fault network development at a reservoir depth of 4.5–7 km is one primary
focus of this study. To achieve this, we investigate 191 focal mechanisms of
the induced seismicity using a cross-correlation-based technique. Our
results indicate that seismicity occurred in three spatially separated
clusters centered around the injection well. We observe a spatiotemporal
migration of the seismicity during the stimulation starting from the
injection well in the northwest–southeast (NW–SE) direction and in
the northeast (NE) direction towards greater depth. The spatial evolution of the
cumulative seismic moment, the distribution of events with Mw≥1,
and the fault plane orientations of focal mechanisms indicate an active
network of at least three NW–SE- to NNW–SSE-oriented permeable zones, which
is interpreted to be responsible for the migration of seismic activity away from
the injection well. Fault plane solutions of the best-constrained focal
mechanisms and results for the local stress field orientation
indicate a reverse faulting regime and suggest that seismic slip occurred on
a sub-parallel network of pre-existing weak fractures favorably oriented
with the stress field, striking NNW–SEE with a dip of 45∘ ENE
parallel to the injection well.
Introduction
Deep geothermal energy is considered a potential source of low-CO2-emission energy to replace fossil fuels. The successful development
of deep geothermal reservoirs is crucial for the economic production of hot
fluids for energy generation. However, crystalline basement rocks hosting
deep geothermal reservoirs in general are low-porosity and low-permeability
formations. In enhanced geothermal systems (EGSs) hydraulic stimulation with
massive fluid injection is applied to improve reservoir permeability (e.g.,
Giardini, 2009). Fluid injection at depth in EGS stimulations and in
wastewater disposal is commonly associated with induced seismicity (e.g.,
Ellsworth, 2013; Majer et al., 2012). Successful mitigation of an induced
seismic hazard is important for public acceptance of geothermal projects, as
there is significant concern related to the occurrence of larger induced
earthquakes during previous EGS projects, e.g., in Basel and St. Gallen,
Switzerland (e.g., Giardini, 2009; Diehl et al., 2017), and most recently in
Pohang, South Korea (Hofmann et al., 2019; Ellsworth et al., 2019).
A well-designed seismic network is a prerequisite for high-resolution data
acquisition, real-time seismic monitoring, and analysis of induced seismicity
(e.g., Bohnhoff et al., 2018). Subsequent feeding of seismic data into a
traffic-light system (TLS) may substantially contribute to mitigating the
associated seismic hazard and risk. A successful and safe approach to
stimulation of the world's deepest EGS in the metropolitan area of Helsinki
was recently presented by Kwiatek et al. (2019). Over 49 d in summer
2018, the St1 Deep Heat Company injected more than
18 000 m3 of water at 6.1 km of depth. An M 2.1 red alert
threshold of the TLS defined by the local authorities was successfully
avoided by a careful adjustment of the hydraulic energy input in response to
real-time monitoring of the spatiotemporal evolution of seismicity. The
largest seismic event was confined to a moment magnitude of Mw 1.9 (Ader
et al., 2019; Kwiatek et al., 2019).
High-quality state-of-the art analysis of induced seismic waveform data is
crucial for a detailed reservoir characterization (Kwiatek et al., 2013).
High-precision locations of hypocenters are typically obtained by applying
relocation techniques such as the double-difference method (Waldhauser and
Ellsworth, 2000). Using relocated data, a precise spatiotemporal evolution
of induced seismicity can be tracked, providing insight into fluid migration
pathways in the reservoir (e.g., Kwiatek et al., 2015; Diehl et al., 2017).
In addition, seismic source parameters such as seismic moment and source
size provide crucial insights into the fracture network geometry.
Bentz et al. (2020) recently showed that many EGS fluid injections display
an extended period of stable evolution of the cumulative seismic moment.
Following Galis et al. (2015), this indicates the growth of self-arrested
ruptures in contrast to unstable increases in seismic moments resulting in
runaway ruptures that are only limited by the size of tectonic faults. Thus,
unusual trends or potential changes in the seismic moment evolution may
provide information on growth and activation of ruptures and thus also on
the anthropogenic seismic hazard and subsequent risk. For example, Bentz et
al. (2020) observed a steep and non-stabilizing increase in the cumulative
seismic moment, potentially signifying unbound rupture propagation during
stimulation for the Pohang EGS project. Dynamic source characteristics of
seismic events including radiated energy, stress drop, and apparent stress
allow the evaluation of seismic injection efficiency (Maxwell et al., 2008) and the
estimation of the energy budget of a stimulation campaign. Moreover, focal
mechanisms provide important information for hazard assessment, as they can
illuminate activation of large pre-existing structures such as major and
potentially critically pre-stressed faults (e.g., Deichmann and Giardini,
2009; Ellsworth et al., 2019). Using focal mechanisms, Ellsworth et al. (2019) showed that induced seismicity activated a fault zone, which
ultimately triggered the large Mw 5.5 earthquake in Pohang. The authors
suggested that seismic analysis performed during stimulation sequences may
provide early information on increasing seismic hazard. In addition, stress
tensor inversion of focal mechanism data using, e.g., the MSATSI
(Martínez-Garzón et al., 2014) or BRTM (D'Auria and Massa, 2015)
approaches allows the estimation of potential changes in the local stress
field but requires high-quality seismic waveform data from dense local
seismic networks. Studies of the spatial and temporal variations of the
stress field orientation contribute to understanding complex
seismo-mechanical processes occurring in the reservoir during injection
(Kwiatek et al., 2013). Martínez-Garzón et al. (2013) first
observed a clear correlation of temporal stress changes in response to high
injection rates at The Geysers geothermal field.
In this study we present a refined high-resolution dataset of seismicity
induced during stimulation of the world's deepest EGS in the
Helsinki suburban area in 2018 (Kwiatek et al., 2019; Ader et al., 2019;
Hillers et al., 2020). The data were collected using a combined seismic
network of individual sensors in shallow boreholes framing the injection
site combined with a multi-level vertical geophone array at ≥ 2 km
of depth. Our dataset expands, refines, and completes the original study of
Kwiatek et al. (2019). We include seismic events that occurred after the
end of the hydraulic stimulation and refine the seismic catalog using
double-difference relocation with a new velocity model derived from a
post-injection vertical seismic profiling (VSP) campaign. To analyze the
structural complexity of the reservoir, we investigate the spatiotemporal
seismicity evolution as well as the temporal and spatial distribution of the
seismic moment release during and after stimulation. This analysis is
supported by an extensive catalog of source mechanisms derived from a
cross-correlation-based technique. Information on the local stress field
orientation is derived from seismicity data. We discuss the evolution of
potentially permeable zones in the reservoir and the reactivation of a
network of small-scale fractures during and after stimulation.
Methodology
Expanding the study of Kwiatek et al. (2019), we enhanced, reprocessed, and
relocated the original seismic catalog to also include post-injection
events between 22 July and 24 September. During and after the
stimulation, induced seismicity was monitored by a dense seismic network of
three-component sensors consisting of a 12-level vertical borehole array and 12 near-surface seismometers with full azimuthal coverage. The
borehole array with 15 Hz sensors, sampled at 2 kHz, was installed at a
depth from 1.95 to 2.37 km in the monitoring well OTN-2 close to the injection
well OTN-3, whereas the 4.5 kHz near-surface seismometers, sampled at 500 Hz, were
placed in wells with depths from 0.3 to 1.15 km and lateral distances of
0.6 to 8.2 km around the injection well (Fig. 1).
Seismic network used for monitoring the stimulation in 2018. (a) Map view showing the near-surface geophones framing the EGS site with the
injection borehole OTN-3 and the OTN-2 well drilled in 2019 and 2020. The radius of
concentric circles represents the distance between the end of the OTN-3 borehole and
each station. (b) Side view of the boreholes with the geophone array placed
at the already existing part of the OTN-2 well. The injection intervals S1–S5 of
the stimulation in 2018 are color-coded at the end of the injection
borehole. For further details about the location of the EGS site in the suburban
area of Helsinki in Finland, see Kwiatek et al. (2019).
The reprocessed seismic catalog, with a description of its properties, is
available as a separate data publication (Leonhardt et al., 2021) and consists
of 5456 events with Mw≥-2.47 that were detected and located
during and after the stimulation (industrial monitoring) and reprocessed in
our study. A total of 55 707 events with Mw≥-0.95 were further
detected during and after the stimulation but were not located or processed
later on. These were also included in the published seismic catalog. For a further
explanation of the original seismic catalog, see Kwiatek et al. (2019).
Hypocenter locations
The enhanced sub-catalog of 5456 events including 946 post-stimulation
events was reprocessed by applying a new updated 1D layered velocity model
developed from P-wave onset times of calibration shots obtained during a
post-injection VSP campaign (Fig. S1; see also Leonhardt et al., 2021). Due
to a low signal-to-noise (S/N) ratio of the VSP data, the S-wave arrival
times could not be determined. Thus, the VP/VS ratio was optimized
by a trial-and-error procedure, whereby we ultimately constrained a
VP/VS ratio of 1.71 that minimized the cumulative residual errors of
all located events and at the same time kept the first induced events close
to corresponding injection well OTN-3.
The hypocenter locations were estimated using the equal differential time
(EDT) method (Zhou, 1994; Font et al., 2004; Lomax, 2005) and the new
VSP-derived velocity model. In addition, station corrections were applied.
The minimization of travel time residuals,
Tjth-Tith-Tjobs-TiobsL2=min,
where Tth and Tobs are all unique pairs (i,j) of theoretical and
observed travel times of P- and S-phases, was resolved using the Simplex
algorithm (Nelder and Mead, 1965; Lagarias et al., 1998) . A total of 2958
reprocessed events were located around the injection well OTN-3 at an epicentral
distance of less than 5 km and at a depth of 4.5 to 7 km. The hypocenters of
these events were included in the reprocessed and published data catalog.
To further refine the quality of hypocenter locations, 2178 of the 2958
absolute located events with at least 10 P-wave and 4 S-wave picks were
selected, and the double-difference relocation technique (hypoDD) was applied
using the new VSP-derived velocity model (Waldhauser and Ellsworth, 2000).
An iterative least-square inversion was used to minimize residuals of
observed and predicted travel time differences for event pairs calculated
from the existing P- and S-wave picks of the selected catalog data. The
residuals were minimized in 10 iterations steps. For the last iteration,
the maximum threshold for travel time residuals was set to 0.08 s, and the
maximum distance between the catalog linked event pairs was defined as
170 m. With the hypoDD method 1986 events were relocated, representing 91 %
of the selected 2178 events. The residuals of the relocations have a root
mean square error of 9 ms. The relocation uncertainties were then assessed
using a bootstrap technique (Waldhauser and Ellsworth, 2000; Efron, 1982),
leading to a relative location precision not exceeding ± 52 m for
95 % of the catalog.
Source mechanisms
To address the structural complexity of the reservoir in close proximity to
the injection borehole below 4.5 km of depth, source mechanisms were determined
for a selected subset of events. For the 63 events with the largest moment
magnitudes located within the main (deepest) hypocenter cluster we first
manually picked the P-wave onset polarities on the vertical component
seismograms of all available stations. All waveforms were first filtered
with a second-order 120 Hz low-pass Butterworth filter. The same approach
was applied to the 25 strongest events of the two shallower hypocenter
clusters (see Fig. 3). The focal mechanisms (FMs) were determined using the
HASH software (Hardebeck and Shearer, 2002). For each fault plane solution
(FPS), associated uncertainties in a form of acceptable solutions are provided,
calculated by perturbing take-off angles and azimuths by up to 3∘
(95 % confidence interval) to simulate the hypocenter location and
velocity model uncertainties, respectively.
Aiming to increase the catalog of focal mechanisms, we extended the focal
mechanism calculations to smaller events with lower S/N ratios using the
cross-correlation-based technique of Shelly et al. (2016). An additional 297 small events with a lower S/N ratio were processed. To this end, the waveforms
from a template set of 70 events with manually picked P-wave polarities were used to
recover relative polarities of a target set of waveforms from 297 events,
including 45 post-stimulation events and 18 events with manually picked
polarities. The waveforms of the events of both sets were first
preprocessed by focusing on the P-wave polarities obtained from the vertical
components of all available stations. Seismograms were filtered with a
second-order 120 Hz low-pass Butterworth filter and a window length of 0.064 s, including 0.012 s before the P-wave first motion. After a few trials, the
low-pass Butterworth filter was fixed to 80 Hz for three stations in the
satellite network due to a higher quality of the estimated polarity results
for these stations. Considering the stations separately, each extracted
waveform from the target set was cross-correlated with all remaining waveforms
forming the template set. This resulted, for a particular station and target event, in a
vector of 70 cross-correlation (CC) coefficients, with the sign representing
the relative polarities between the target and template P-wave onsets for a particular
station. Following Shelly et al. (2016), if the lag time of the largest
cross-correlation peak was lower than 0.2 times the extracted wavelength,
the CC was accepted and used as a relative polarity estimation between
target event and template. The polarity estimates obtained from the CC values between the
picked template and target events are relative and weighted by the absolute value of
the corresponding cross-correlation coefficient. Thus, the sign of the estimated
polarity of the target event will be positive if the template and the target event have the same
P-wave first motion.
For each station k, the vectors containing relative polarity estimates
between one target event i and all templates j were gathered in a i-by-j matrix. A singular value decomposition
(SVD) was applied to the relative estimated polarity matrix of each
station k to extract the strongest common signal of any target event obtained by
the first left singular vector of the SVD (Shelly et al., 2016; Rubinstein
and Ellsworth, 2010). The estimated first left singular vectors for each
station k are gathered in an i-by-k matrix:
PPik=pp11⋯pp1k⋮⋯⋮ppi1⋯ppik,
which then represents the most reasonable but still relative polarity
pattern of each target event.
To reduce the polarity ambiguity of the events, we considered 18 events with
known manually picked polarities included in the target event set. For each
station k, the SVD-derived polarities of these events were compared with
manually picked polarities to investigate whether the polarities have
similar or opposite signs. In the case of the same polarities, the SVD-derived
polarities of other events should also show the right sign for the
particular stations.
Estimated polarity patterns of the events were then used to calculate focal
mechanisms. For further investigation we only considered events with a good
quality of estimated focal mechanisms no matter if the polarities were
manually picked or estimated. Thus, we only used events with focal
mechanisms that have root mean square fault plane uncertainties less than or
equal to 35∘ (Hardebeck and Shearer, 2002). The final catalog of
focal mechanisms includes 191 events with either a manual or estimated
polarity pattern and is presented with associated uncertainties in the data
publication (see Leonhardt et al., 2021).
Complexity of source mechanisms
To investigate the variability of the estimated focal mechanisms, we first
calculated the principal axis directions of the double-couple seismic moment
tensor derived from the focal mechanism for each event. To quantify the level of
similarity of any two focal mechanisms, we calculated the 3D Kagan rotation
angle between the principal axis directions of both events (Kagan, 1991,
2007; Tape and Tape, 2012). Low values of the Kagan angle (<20∘) suggest that focal mechanisms of two events are similar. To
further group events into families with similar source mechanisms, an
unsupervised classification of the 191 events was performed using a
hierarchical cluster analysis based on the similarity of estimated Kagan
rotation angles. Thus, the measurement of proximity PR of any two focal
mechanisms was defined as a distance metric:
PRij=1-cos(θijrot)1.5,
where θijrot is a matrix containing the estimated rotation
angles between any focal mechanism pair ij. In the following, the dendrogram
tree based on the hierarchical clustering was used to separate focal
mechanisms into different families.
To investigate the local stress field orientation in the reservoir
surrounding the injection well, we applied the linear stress inversion
method MSATSI (Martínez-Garzón et al., 2014) and the
Bayesian-analysis-based and nonlinear stress inversion method BRTM of D'Auria
and Massa (2015). In both methods, the strike, dip, and rake angles of the
fault plane solutions from the focal mechanisms were used to invert the
orientation of three stress axes. A relative measure of the stress magnitude
is obtained by the stress shape ratio R (e.g., Hardebeck and Michael, 2006;
Lund and Townend, 2007):
R=σ1-σ2σ1-σ3.
ResultsSeismic catalog update
The moment magnitudes of the absolute located and relocated seismicity are
plotted with time during and after shut-in as grey and orange dots in Fig. 2. The five different stimulation phases (P1–P5) performed in 2018 are also
shown in Fig. 2 in combination with the wellhead pressure and seismic event
rate. Further details of the stimulation protocol and seismicity evolution
are presented by Kwiatek et al. (2019), and here we focus on analysis of
post-stimulation seismicity.
Stimulation protocol with moment magnitudes of induced seismicity
during stimulation phases P1–P5 and post-stimulation time period. The
magnitudes of 2958 absolute located and 1986 relocated events are shown as
grey and orange dots, respectively. The solid green line represents the
wellhead pressure during the stimulation. The seismic event rate per day is
shown by the solid blue line.
Hypocenters of relocated events. (a) Map view and (b) SW–NE depth
section. The hypocenters are color-coded with the stimulation phases (see
Kwiatek et al., 2019), and the size corresponds to moment magnitude. Relocated
seismicity that occurred after the stimulation is represented as grey dots.
Areas with large events occurring during stimulation phase P5 and
post-stimulation time are highlighted by red rectangles (see main text for
details). The five injection stages are marked as color bands along the
borehole trace from the bottom of the open hole toward the casing shoe of
the injection well OTN-3 (black). The new OTN-2 well (grey) was drilled in 2019 to
2020 after the stimulation.
The 213 post-injection events with absolute locations were detected during a
time period of 2 months after shut-in of injection, and all had magnitudes
Mw≥-0.7. After shut-in, the seismic event rate started to rapidly
decrease (Fig. 2). This decrease in activity continued until the fifth
day after the end of the injection, followed by a slower decrease thereafter.
During the first 2 d after shut-in, seven events with Mw≥1.0
occurred. The largest event had a magnitude of Mw=1.5 and occurred
directly after bleed-off, followed closely by two Mw 1.3 events. Three
events with Mw≥ 0.9 occurred within the first 11 d of the
post-stimulation phase. Two further Mw>1 events occurred
within 24 h and 17 d after the stimulation ended, one with a moment
magnitude of 1.6 (Fig. 2). The latter events coincided with engineering
operations performed in the injection well.
The updated relocated hypocenters occurred in three spatially separated
clusters elongated in the southeast–northwest (SE–NW) direction and centered
along the injection well, in good agreement with Kwiatek et al. (2019) (Fig. 3). Elongation of the two shallower clusters in the SE–NW direction is
sub-parallel to the local maximum horizontal stress
SHmax=110∘ (Kwiatek et al., 2019; Heidbach et al.,
2016; Kakkuri and Chen, 1992). The main seismicity cluster centers around
the open-hole section of the borehole and spans ∼ 700 m of depth
(Fig. 3b). This exceeds the vertical relocation precision, which is well
constrained due to sensors being located in a vertical borehole. The
spatiotemporal seismicity evolution during the stimulation developed in two
preferential directions starting from the injection well: in the NW–SE direction
sub-parallel to the direction of SHmax and in the northeast (NE)
direction with depth. The relocated post-stimulation events are mainly
located at the outer edges of the clusters following the trend observed
during the stimulation. The post-injection seismicity shows no spatial
migration, and the largest post-stimulation events with magnitudes between
Mw 1.0 and Mw 1.5 occurred at the NNW and SSE outer edge of the main
cluster. These events are located in close proximity to some of the largest
events of the last stimulation phase P5 (red rectangles in Fig. 3a) when
high seismicity rates were observed.
Temporal evolution of cumulative seismic moment
For the stimulation period, the temporal evolution of the cumulative seismic
moment (CM0) release is discussed by Kwiatek et al. (2019). Here, we show the
temporal evolution of the cumulative seismic moment release for a
time period of 30 d during the post-stimulation period and compare it
with the evolution before the shut-in of injection. During the first 2 d of
the post-stimulation period, the increase in CM0 was similar to the
first 2 d of stimulation phases P1–P5 (Fig. 4). Shortly after
bleed-off, the CM0 rapidly increased due to the three Mw≥1
events (Fig. 2). Thereafter, the increase in post-stimulation moment release
was substantially less compared to a similar time period during P1–P5. Only
two single events occurred with Mw≥1 during day 17, seemingly
triggered by post-stimulation engineering operations in the well.
For a time period of 30 d, the temporal evolution of cumulative
seismic moment release for the relocated seismicity is shown for each
injection phase and for the post-stimulation phase.
The temporal evolution of the CM0 separated for each hypocenter
cluster, marked in Fig. 3b, is shown in Fig. 5. For the upper cluster, the
increase in the CM0 is visibly larger for the stimulation phase P1 than
for the other phases. For stimulation phase P2, a substantial increase in
CM0 occurred between days 4 and 5. For the central hypocenter cluster, a
substantial increase in the CM0 is visible for stimulation phases P2, P4,
and P5 at the beginning of day 3 and also for P1 and P4 during day 6. For
both upper and central clusters, the post-stimulation CM0 is
substantially smaller compared to that from injection (Fig. 5a–b). The
CM0 during post-stimulation in the bottom cluster is similar to P2–P5
within the first 2 d and afterwards lower than P2–P5. Inevitably, the
bottom cluster that hosts the majority of the seismic activity also displays
the highest CM0 (Fig. S2). We note that the slopes of the CM0
evolution are similar for the upper and central cluster but steeper for the
bottom cluster (Fig. S2).
Temporal evolution of the cumulative seismic moment release with
time for each of the three hypocenter clusters separately: (a) the uppermost
hypocenter cluster, (b) the central hypocenter cluster, and (c) the deepest
and main hypocenter cluster.
Spatial evolution of cumulative seismic moment
For the spatial distribution of the seismic moment, the area around the
injection well was separated into horizontal bins of 50×50 m. The cumulative
seismic moment of all events within each bin was then investigated by
disregarding the depth. During stimulation, the largest moment release and
level of seismic activity occurred at the center of the main event cluster
at the bottom of the injection well close to the open-hole section
(Fig. 6a–b). Furthermore, larger events in the main cluster tend to locate
at the greatest depths. Interestingly, a NNW–SSE alignment of enhanced
cumulative seismic moment release is visible in the main hypocenter cluster,
in agreement with the preferred NW–SE-trending direction of the two upper
hypocenter clusters. The hypocenters of larger events show a similar
alignment (Figs. 6a, S3). A smaller area at the NNW outer flank of the bottom
hypocenter cluster displays anomalously high CM0 release caused by
large events occurring during the last injection phases and after injection
(red rectangle in Fig. 6a–b). Interestingly, epicenters of two tectonic
seismic events with Mw 1.4 and Mw 1.7 were reported to occur in 2013 a few kilometers NW of the bottom-hole section of well OTN-3 (Kwiatek
et al., 2019).
Spatial evolution of the cumulative seismic moment release of the
relocated seismicity per bins of 50 by 50 m. (a) The cumulative seismic
moment release converted to seismic moment magnitude per bin overlaid by
seismicity with Mw≥1. (b) The number of events that occurred
per bin. A smaller area of anomalously high CM0 release caused by a few
large events is highlighted by red rectangle.
Complexity of source mechanisms
We determined 191 single-event focal mechanisms (Fig. 7). Using the
dendrogram tree based on hierarchical clustering (Fig. S4), events were
separated into three distinct families (I–III) with similar focal mechanism
orientations containing 99, 60, and 27 events, respectively (different
coloring of beach balls in Fig. 7). Five events were not grouped into any of
the three families and thus were not considered any further. Events
belonging to the three families are not separated spatially. Oblique reverse
faulting is the dominant source mechanism type, which is in contrast to the
regional strike-slip regime (Kwiatek et al., 2019). The two largest events
with reverse faulting were classified into family III. Fault plane solutions
from all families indicate a range of preferred SSE–NNW to SW–NE strike
directions, sharing comparable dips ranging approximately 35–50∘ (Fig. 7a
and e). The source mechanisms of only a few events indicate strike-slip
faulting, with two of them occurring after shut-in. A total of 12 estimated
focal mechanisms are post-stimulation events (Fig. 7b, d, and f). The
post-stimulation events contained in the main hypocenter cluster at the
bottom of the well have similar focal mechanisms as events during the
stimulation. In the central hypocenter cluster, two strike-slip events
occurred nearby.
Orthogonal views of estimated focal mechanisms in three different
projections: (a, b) map view, (c, d) side view from the south (180∘),
and (e, f) side view from the NW (290∘) along the direction
of the maximum horizontal stress SHmax=110∘. (a, c, e) All 191 estimated focal mechanisms. (b, d, f) Focal mechanisms of
post-stimulation events. The color code indicates the family obtained. Relocated
seismicity without estimated focal mechanisms is plotted as small grey
dots.
To further explore separation of the focal mechanisms into distinct
families, we analyzed the rotation angle between the principal P and T axes as
a measure of mechanism (dis)similarity. We first calculated the mean fault plane
solution for each family. The strike, dip, and rake values of the mean fault plane
solutions (FPSs) for the families are as follows: 332∘, 47∘, and 43∘ for family I; 32∘, 51∘, and 141∘ for family II; and
67∘, 36∘, and 122∘ for family III, respectively. The focal
mechanisms with mean fault plane solutions and all the best FPSs of each family
are plotted in Fig. 8a–c. Hillers et al. (2020) recently estimated focal
mechanisms for the 14 largest events, and the majority are similar to
family I FMs. The calculated rotation angles between the mean solutions of
families I and II, I and III, and II and III are 71, 59, and
53∘, respectively. Taking into account that focal mechanisms are
assumed to be similar if the Kagan rotation angle is less than 20∘, none of the three families are similar to each other. The difference between
families I and II is the most prominent, whereas rotations I–III and II–III
are comparable. However, despite the mean solutions of different families being
quantitatively distinct, the individual mechanisms are not necessarily very
different (Fig. 8d–f) between families. The total P-axis uncertainties
strongly overlap among the three families. At the same time, the T-axis
uncertainties form three distributions that, when compared between
families, only partially overlap. This overall suggests that the FPSs
may be sensitive to changes in polarities at individual stations located
close to the nodal plane.
(a–c) Mean fault plane solutions (black lines) calculated from the
best FPSs of 99 events forming family I (a), 60 events forming family II (b),
and 27 events forming family III (c). Contributing FPSs from which the mean is
calculated are shown in blue, orange, and green, respectively. The
most repetitive polarity pattern observed at each station is presented as a
black or white dot for positive or negative onsets, respectively. P1 and P2
symbols correspond to the projections of the main and auxiliary fault planes
according to which one is better oriented for failure on the Mohr circle
represented in Fig. 10. (d–f) For each of the families, the mean P and
T axes as well as axes of contributing FPSs are plotted as big and small
white dots, respectively. The HASH-derived uncertainties (95 % confidence
interval) of the P and T axis of all events within each family are shown
using a blue and brown coloring scale, respectively.
In the following, we qualitatively analyze the polarity patterns of events
forming three families. Regardless of whether the polarities are manually picked or estimated, the most repetitive polarity pattern observed at each station
for a particular family is plotted in Fig. 8a–c. For each family and
station, the percentage of FM events showing this repetitive pattern is
presented in Table S1. We first verified the consistency of polarity patterns for
events with manually picked polarities (N= 37, 15, and 15 FPSs for families I, II, and
III, respectively). We noted that the strike-slip mechanisms are attributed to
the least well-constrained focal mechanisms belonging to family II. The main
substantial difference in the polarity patterns across families seems to be
related to polarities observed at two stations, MALM and MUNK (Fig. 8a–c). For
family I, the polarities at these two stations are negative and extremely
consistent among events forming the family (35 out of 37 events display such
a behavior). For family II, we observe MALM and MUNK to have a mostly negative and
positive polarity pattern, respectively. For family III, the situation is
reversed, with MALM and MUNK having a predominantly positive and negative polarity
pattern, respectively. We further qualitatively analyzed the polarity
pattern of events with polarities estimated from the cross-correlation-based
technique of Shelly et al. (2016). Here, the situation is generally further
complicated due to ambiguities in resolving the polarities because of a decreased
signal-to-noise ratio. However, for the majority of the events forming
family I, the resolved focal mechanisms still show a similar polarity
pattern as the most repetitive pattern shown in Fig. 8a, with only
incidentally changing polarities at stations UNIV and RUSK, which are both located further away from the EGS site than other sensors and thus
display a lower signal-to-noise ratio. The pattern of resolved polarities
for family II is generally comparable to the most repetitive polarity
pattern shown in Fig. 8b. However, 18 out of 45 events have negative
estimated polarities for MUNK; thus, the resolved polarity patterns seem to vary
more in comparison to those of family I. The events with estimated polarities
for family III have the same patterns for stations MALM and MUNK as the most
repetitive pattern in Fig. 8c except for two events. However, other stations
(e.g., UNIV, RUSK, and LASS) with a lower signal-to-noise ratio sometimes display varying
resolved polarities for families II and III. We suppose that (1) the
attribution of focal mechanisms to a particular family is substantially
dependent on the polarity pattern of a limited number of stations that are
located close to the nodal planes, and (2) family I focal mechanisms seem the
most stable.
Using the BRTM and MSATSI stress tensor inversion methods based on 191 focal
mechanisms, we estimated the local stress field orientation. The variability
of FMs to constrain the stress field inversion is given due to high Kagan
rotation angles between the mean FPSs of the three families with
53 to 71∘. The BRTM results show that the maximum
principal stress axis σ1 is oriented almost horizontally, with a
trend of 279∘ and a plunge of 4∘ (Fig. 9). The minimum
principal stress axis σ3 has a trend and plunge of
185∘ and 67∘, respectively. The trend of σ1 and the stress shape ratio are comparable to their independent
estimates using wellbore breakouts and minifrac shut-in pressures (see
Backers et al., 2016; Kwiatek et al., 2019, for details), for which
SHmax= N110∘ E and R=0.46 were reported. Using the
MSATSI method, the trend and plunge of σ1 is calculated as
271∘ and 11∘, respectively. Thus, the estimated trend
of σ1 deviates ∼ 20∘ from the maximum
horizontal stress SHmax. The minimum principal stress axis
σ3 is oriented with a trend of 76∘ and a plunge of
79∘. The estimated stress shape ratio is R=0.72, which is larger
with respect to the BRTM and geophysical estimates.
Stereonet of the estimated local stress field using the BRTM. White
upward- and downward-pointing triangles represent maximum and minimum
principal stress axes σ1 and σ3, respectively.
Black arrows represent maximum horizontal stress SHmax in the
reservoir.
The stress obtained by focal mechanism inversion represents a local reverse
faulting regime. This is in contrast to the regional strike-slip regime
estimated from regional stress and borehole data (Kwiatek et al., 2019).
Only the focal mechanisms of a few events present a dominant strike-slip
faulting, which are typically smaller events with a less well-constrained
polarity pattern.
Discussion
Analysis of the seismic data suggests that fluid injection was performed
into a complex network of small-scale pre-existing and distributed fractures
and minor faults rather than activating a single major fault (Kwiatek et
al., 2019). In an effort to characterize the structural complexity of the
reservoir in detail, we compiled a high-resolution dataset of hypocenters
and single-event focal mechanisms by enhancing and refining the original
seismic catalog.
The relocated events of our updated catalog show three separated spatial
hypocenter clusters along the injection well, in good agreement with Kwiatek
et al. (2019) and Hillers et al. (2020). Hillers et al. (2020) used seismic
data collected from an independent surface-based seismic network of dense
sub-arrays, whereas Kwiatek et al. (2019) used the same seismic network as
we do but a simplified velocity model and slightly different
VP/VS ratio. The hypocentral depths of the events vary slightly
between this and previous studies. We found that differences between
absolute locations among these catalogs are likely explained by variations
in VP/VS ratios and velocity models.
We also provide the first analysis of post-stimulation events, expanding the
seismic catalog to investigate potential changes in the seismicity pattern
from the stimulation to the post-stimulation period. Compared to the seismicity
occurring during the stimulation, the post-stimulation seismicity shows no
spatiotemporal migration. The largest post-stimulation events occurred at
the NNW and SSE outer edges of the main hypocenter cluster where
anomalously higher seismicity rates and larger events were also observed during
the last stimulation phase P5 (see Fig. 3). For the main hypocenter cluster,
the temporal evolution of the post-stimulation CM0 shows similarities
to the injection period until bleed-off of the well, with only small changes
thereafter. This suggests that seismicity is driven by the elevated pressure
in the reservoir due to the previous hydraulic pumping (increased stored
elastic energy). However, hypocenter propagation requires active pumping.
This is indicated by a much smaller residual increase in CM0, no
further migration of the seismicity after bleed-off, and a decrease in
reservoir fluid pressure.
The spatiotemporal seismicity evolution during stimulation and the
spatial distribution of the cumulative seismic moment release indicate clear
alignment of the events in the NW–SE direction in the two shallower hypocenter
clusters, which could signify activation of permeable zones along faults or
joints oriented in this direction. The existence of these zones is supported by
the results of OTN-3 well logging, wherein intervals of highly damaged rocks were
detected that roughly coincide with the intersection of the upper seismicity
clusters and the well path. For the largest bottom seismicity cluster, the
relocated seismicity is distributed diffusively around the injection well.
However, larger seismic events form a distinct alignment along a NNW–SSE
direction (Figs. 6a, S3) with post-stimulation events clearly located at the
perimeter of the narrow zone (Fig. S3). This alignment indicates activation
of another permeable zone similar to the two upper ones. The NNW–SSE-trending orientation coincides with an abundance of very similar focal
mechanisms from the best-constrained family I events with a strike direction
nearly identical to the NNW–SSE alignment of hypocenters. Moreover, two
natural micro-earthquakes with Mw 1.7 and Mw 1.4 occurred in 2013 a
few kilometers NNW from the well (Kwiatek et al., 2019). Although there is
no detailed information available on their depths due to limited coverage of
the seismic network at their origin time, their epicentral location
coincides with the NNW perimeter of the bottom NNW–SSE alignment hosting
large induced seismicity events as well. These observations suggest that the
stimulation activated at least three prominent NW–SE- to NNW–SSE-oriented
permeable zones of sub-parallel fractures or faults that are responsible for
seismicity migration away from the injection well during the stimulation.
The deepest NNW–SSE-trending zone is buried in more disperse seismic
activity forming the bottom cluster and hosts the largest induced earthquakes (and
likely some natural at earlier times). The fact that the largest events
occurred in the deepest permeable zone may simply be related to the highest
expected pore pressure perturbation in this volume due to injection and
migration of fluids. Kwiatek et al. (2019) speculated that the maximum event
magnitude is either limited by available fault sizes or the strength of the
faults. The total length of the NNW–SSE-trending permeable bottom zone
(∼ 650 m, Fig. S3), clearly marked by the numerous and very
similar focal mechanisms, is much larger than the average size of a single
Mw 2 earthquake (∼ 80 m diameter) with even lower
relocation precision. We therefore suggest that the upper limit on the maximum
magnitude is related to the low fault strength.
For the main hypocenter cluster, the seismicity migrates towards the NE and
towards greater depths, dipping in the same direction as the inclined
portion of the OTN-3 well (Fig. 3). The propagation of seismicity may signify
activation of small-scale fractures striking NNW–SSE and dipping along the
injection well. This is again supported by the catalog of source mechanisms
forming family I events (see Figs. 7 and 8a). To further understand this
striking observational and qualitative agreement of family I fault planes
with spatial distribution and evolution of seismic activity, we tested which
family of focal mechanisms is better oriented for failure within the local
stress field estimated using the BRTM. The resulting BRTM stress tensor and
estimated stress shape ratio R=0.53 have been used to create a Mohr diagram
of the 3D stress state (Fig. 10) (see Vavryčuk, 2014;
Martínez-Garzón et al., 2016). We projected
estimated FPSs into the Mohr diagram, which revealed fault plane orientations
with respect to the stress field. Optimally oriented fault planes, located
generally closer to the left part of the Mohr diagram, are more likely to be
activated (e.g., Vavryčuk, 2011), e.g., in the presence of enhanced pore
fluid pressure. To quantify the proximity to failure criterion, we assumed a
friction coefficient of μ=0.7 as a mean value for faults in the
Earth's crust (Vavryčuk, 2011). While projecting the preferred fault
plane out of the two nodal planes from each fault plane solution, we used
the nodal plane that displayed a higher instability coefficient I (see
Vavryčuk, 2014; Martínez-Garzón et al., 2016):
I=τ+μ(σn+1)μ+1+μ2,
with τ and σn as the normalized shear and normal
tractions, respectively, and μ as the friction coefficient.
Deviatoric Mohr circle representing the local stress field, with
the fault plane solutions having the highest fault instability coefficient
of the estimated focal mechanisms. The stress inversion resulted in a stress
ratio of R=0.53. Events with Mw≥1 and Mw<1 are
plotted as triangles and circles, respectively. Filled and unfilled markers
represent events with manually picked and estimated polarities,
respectively. The mean and its auxiliary fault plane solution of each family
are plotted as filled large dots labeled as P1 and P2, respectively. Most
family I events (blue symbols) occurred on critically stressed faults.
Clearly, FPSs from family I are the most favorably oriented with respect to
the local stress field (blue points and triangles in Fig. 10), as also
indicated by the highest fault instability coefficients (Fig. 11). It turned
out that the most optimally oriented fault plane is always the one trending
NNW–SSE and dipping approximately in the direction of the inclined portion of
the OTN-3 well (indicated by P1 nodal planes in Fig. 8a). This is also confirmed by
the mean solution of family I (332∘/47∘ plane, blue P1
marker in Fig. 10) displaying the highest instability (Table 1). However,
the fault planes represented by the auxiliary plane of the mean
solution of family I are also quite favorably oriented (blue P2 marker in Fig. 10). Some of the family III events are also quite favorably oriented with
the stress field. We note that instabilities in the auxiliary planes of mean
FPSs for families I and III are similar (green and blue P2 dots in Fig. 10,
Table 1), in agreement with their mean auxiliary nodal plane orientations of
210∘ and 60∘ (P2 in Fig. 8b–c). Qualitatively, nodal planes
from family II seem to be mostly unfavorably oriented with the stress field
(orange points and triangles in Fig. 10), as indicated by the lowest
instability coefficients (Fig. 11). However, some P1 nodal planes
strike N–S (see Fig. 8b) and thus show quite similar orientations as
the P1 FPSs of family I (Fig. 8a), leading to higher instability
coefficients for these planes (orange dots and triangles close to the blue and
green P2 marker in Fig. 10). Here, we found 19 events of family II that show
similar polarity patterns as those observed for family I events, with
an opposite polarity only for station MUNK.
Highest fault instability coefficient of any of the two FPSs for
each FM event plotted with moment magnitude. Events with manually picked and
estimated polarities are plotted as filled and unfilled circles,
respectively.
Fault instabilities of the mean fault plane solution and its auxiliary
plane for each family.
The performed analysis of fault instability clearly showed that high-quality
focal mechanisms constituting family I events display a comparable oblique
reverse component and optimally oriented fault planes striking approximately
NNW–SSE and dipping around 45∘. These fault plane orientations
are in agreement with the estimated stress field, and they explain the
spatiotemporal evolution of seismicity well with the corresponding fluid migration
pattern. The 2018 seismicity activated a pre-existing network of small-scale
parallel fractures dipping to the ENE, in agreement with the dip direction of
the inclined part of the injection well. Fault planes striking NNE–SSW to
NE–SW and dipping around 60∘ were also indicated to be quite
favorably oriented with the stress field represented by the auxiliary plane
of the mean FPSs for families I and III. Drill bit seismic data suggest the
existence of a steeply dipping NE–SW-striking structure, which might have been
activated by the 2018 seismic activity. We note that the FM results are in good
agreement with a limited number of 14 focal mechanisms of the strongest
events presented in Hillers et al. (2020), with all but one displaying
reverse faulting motions.
Summary and conclusions
We present a new seismic catalog for the geothermal stimulation in Helsinki
2018, by determining new locations and relocations on the basis of the new
VSP-based velocity model, and include the post-stimulation seismicity,
resulting in a catalog with 5456 events. The catalog is extended by the
list of detections, amounting to 61 163 events provided to the scientific
community (see Leonhardt et al., 2021). The magnitude of completeness of the
entire catalog is Mc=-1.10. The catalog is supplemented by 191 focal
mechanisms, calculated using polarity-based and cross-correlation-based
methods, and is used to discuss the structural complexity of the reservoir.
Spatial migration of the seismicity is driven by enhanced pore fluid
pressure due to active injection, as no spatial migration of the
post-stimulation seismicity after bleed-off is found. Until shortly after
the bleed-off, the increase in the cumulative moment release of the
post-stimulation seismicity with time is comparable to the slope of the
CM0 during individual stimulation phases but substantially less
afterwards. This is especially observed for the seismicity of the deepest
hypocenter cluster.
An activated network of at least three NW–SE- to NNW–SSE-oriented fracture
zones of up to 200 m thickness seems to be responsible for the significant
seismic activity migration towards the NW–NNW and SE–SSE away from the injection
well. The deepest fracture zone also hosts many of the larger seismic events
with magnitudes exceeding Mw≥1; this suggests elevated fluid volume
and pore fluid pressure, leading to the accumulation of hydraulic energy in this
area that is relaxed in larger seismic events.
The best-constrained focal mechanisms strike NNW–SSE, in agreement with
the orientation of three fracture zones. Most of these mechanisms display
∼ 45∘ ENE-dipping oblique thrust-fault planes that
were found to be critically stressed in the resolved local stress field.
These fault kinematics explain the NNW–SSE migration of seismicity well along
damage zones, in addition to the downward migration of events towards the NE–NNE
along the dip direction vector of the inclined portion on the injection well.
We conclude that seismic slip occurs on a sub-parallel network of favorably
oriented pre-existing but weak fractures striking in the NNW–SSE direction and
dipping 45∘ ENE. The localization of seismic moment release in
NNW–SSE-trending zones suggests the existence of NNW–SSE-trending damage
structures or lithological differences that increase the mobility of fluids
in confined parts of the reservoir.
Data availability
The seismic event catalog, with an associated description of its basic
statistical and spatiotemporal properties, is available through GFZ data
services (10.5880/GFZ.4.2.2021.001) as a separate data
publication (Leonhardt et al., 2021). For the event detections, the catalog
contains origin times as well as local and moment magnitudes. For located events, the
catalog contains origin times, local and moment magnitudes, and absolute
locations in a local Cartesian coordinate system; for relocated events it also contains
the double-difference relocated locations in a local Cartesian coordinate
system. The fault plane solutions (strike, dip, and rake), with associated
uncertainties of estimated focal mechanisms, are also included in the data
catalog.
The supplement related to this article is available online at: https://doi.org/10.5194/se-12-581-2021-supplement.
Author contributions
ML was responsible for data reduction, analysis and results interpretation, the draft version of
the paper, and associated data publication. GK and PMG conducted data
analysis, results interpretation, and paper correction. MB, GD, and
PH were responsible for results interpretation and paper correction. TS led
project management, drilling and stimulation program development and
managing, and paper correction.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
St1 Deep Heat Oy is acknowledged for providing the data for our study. We
thank Ilmo Kukkonen and Peter Malin for the valuable discussions.
Financial support
Grzegorz Kwiatek
acknowledges founding from the DFG (German Science Foundation) under grant KW84/4-1.
Patricia Martínez-Garzón acknowledges funding from the Helmholtz Association through the
Helmholtz Young Investigators Group “Seismic and Aseismic Deformation in
the brittle crust: implications for Anthropogenic and Natural hazard”
(http://www.saidan.org, last access: 26 February 2021).
The article processing charges for this open-access publication were covered by a Research Centre of the Helmholtz Association.
Review statement
This paper was edited by Michal Malinowski and reviewed by two anonymous referees.
ReferencesAder, T., Chendorain, M., Free, M., Saarno, T., Heikkinen, P., Malin, P.,
Leary, P., Kwiatek, G., Dresen, G., Bluemle, F., and Vuorinen, T.: Design
and implementation of a traffic light system for deep geothermal well
stimulation in Finland, J. Seismol., 24, 991–1014,
10.1007/s10950-019-09853-y, 2019.
Backers, T. and Meier, T.: Stress field modeling for the planned St1 Deep
Heat geothermal wells for Aalto University, Finland, Report
A1601/St1/160508fr, 2016.Bentz, S., Kwiatek, G., Martínez-Garzón, P., Bohnhoff, M., and
Dresen, G.: Seismic moment evolution during hydraulic stimulations,
Geophys. Res. Lett., 47, e2019GL086185,
10.1029/2019GL086185, 2020.
Bohnhoff, M., Malin, P., ter Heege, J., Deflandre, J.-P., and Sicking, C.:
Suggested best practice for seismic monitoring and characterization of
non-conventional reservoirs, First Break, 36, 59–64, 2018.D'Auria, L. and Massa, B.: Stress inversion of focal mechanism data using a
bayesian approach: A novel formulation of the right trihedra method,
Seismol. Res. Lett., 86, 1–10,
10.1785/0220140153, 2015.Deichmann, N. and Giardini, D.: Earthquakes induced by the stimulation of an
enhanced geothermal system below Basel (Switzerland), Seismol. Res.
Lett., 80, 784–798, 10.1785/gssrl.80.5.784, 2009.Diehl, T., Kraft, T., Kissling, E., and Wiemer, S.: The induced earthquake
sequence related to the St. Gallen deep geothermal project (Switzerland):
Fault reactivation and fluid interactions imaged by microseismicity, J. Geophys. Res.-Solid Ea., 122, 7272–7290,
10.1002/2017JB014473, 2017.Efron, B.: The jackknife, the bootstrap and other resampling plans, CBMS-NSF regional conference series in applied mathematics, 38, SIAM, Philadelphia,
10.1137/1.9781611970319, 1982.Ellsworth, W. L.: Injection-induced earthquakes, Science, 341, 1225942,
10.1126/science.1225942, 2013.Ellsworth, W. L., Giardini, D., Townend, J., Ge, S., and Shimamoto, T.:
Triggering of the Pohang, Korea, earthquake (Mw 5.5) by enhanced geothermal
system stimulation, Seismol. Res. Lett,, 90, 1844–1858,
10.1785/0220190102, 2019.Font, Y., Kao, H., Lallemand, S., Liu, C.-S., and Chiao, L.-Y.: Hypocentre
determination offshore of eastern Taiwan using the maximum intersection
method, Geophys. J. Int., 158, 655–675,
10.1111/j.1365-246X.2004.02317.x, 2004.Galis, M., Pelties, C., Kristek, J., Moczo, P., Ampuero, J.-P., and Mai,
P. M.: On the initiation of sustained slip-weakening ruptures by localized
stresses, Geophys. J. Int., 200, 890–909,
10.1093/gji/ggu436, 2015.Giardini, D.: Geothermal quake risks must be faced, Nature, 462, 848–849,
10.1038/462848a, 2009.Hanks, T. C. and Kanamori, H.: A moment magnitude scale, J.
Geophys. Res.-Solid Ea., 84, 2348–2350,
10.1029/JB084iB05p02348, 1979.Hardebeck, J. L. and Michael, A. J.: Damped regional-scale stress inversions:
Methodology and examples for southern California and the Coalinga aftershock
sequence, J. Geophys. Res.-Solid Ea, 111, B11310,
10.1029/2005JB004144, 2006.Hardebeck, J. and Shearer, P.: A new method for determining first-motion
focal mechanisms, B. Seismol. Soc. Am., 92,
2264–2276, 10.1785/0120010200, 2002.Heidbach, O., Mojtaba, R., Reiter, K., Ziegler, M., and WSM Team: World
stress map database release 2016 V.1.1, GFZ Data Services,
10.5880/WSM.2016.001, 2016.Hillers, G., Vuorinen, T., Uski, M., Kortström, J., Mäntyniemi, P.,
Tiira, T., Malin, P., and Saarno, T.: The 2018 geothermal reservoir
stimulation in Espoo/Helsinki, southern Finland: Seismic network anatomy and
data features, Seismol. Res. Lett., 91, 770–786,
10.1785/0220190253, 2020.Hofmann, H., Zimmermann, G., Farkas, M., Huenges, E., Zang, A., Leonhardt,
M., Kwiatek, G., Martinez- Garzon, P., Bohnhoff, M., Min, K.-B., Fokker, P.,
Westaway, R., Bethmann, F., Meier, P., Yoon, K.S., Choi, J. W., Lee, T. J.,
and Kim, K. Y.: First field application of cyclic soft stimulation at the
Pohang Enhanced Geothermal System site in Korea, Geophys. J.
Int., 217, 926–949, 10.1093/gji/ggz058, 2019.Kagan, Y. Y.: 3-D rotation of double-couple earthquake sources, Geophys.
J. Int., 106, 709–716,
10.1111/j.1365-246X.1991.tb06343.x, 1991.Kagan, Y. Y.: Simplified algorithms for calculating double-couple rotation,
Geophys. J. Int. 171, 411–418,
10.1111/j.1365-246X.2007.03538.x, 2007.Kakkuri, J. and Chen, R.: On horizontal crustal strain in Finland, B. Geod., 66, 12–20, 10.1007/BF00806806, 1992.Kwiatek, G., Bohnhoff, M., Martínez-Garzón, P., Bulut, F., and
Dresen, G.: High-resolution reservoir characterization using induced
seismicity and state of the art waveform processing techniques, First Break,
31, 81–88, 10.3997/1365-2397.31.7.70359, 2013.Kwiatek, G., Martínez-Garzón, P., Dresen, G., Bohnhoff, M., Sone,
H., and Hartline, C.: Effects of long-term fluid injection on induced
seismicity parameters and maximum magnitude in northwestern part of The
Geysers geothermal field, J. Geophys. Res.-Solid Ea., 120,
7085–7101, 10.1002/2015JB012362, 2015.Kwiatek, G., Saarno, T., Ader, T., Bluemle, F., Bohnhoff, M., Chendorain,
M., Dresen, G., Heikkinen, P., Kukkonen, I., Leary, P., Leonhardt, M.,
Malin, P., Martínez-Garzón, P., Passmore, K., Passmore, P.,
Valenzuela, S., and Wollin, C.: Controlling fluid-induced seismicity during
a 6.1-km-deep geothermal stimulation in Finland, Sci. Adv., 5,
eaav7224, 10.1126/sciadv.aav7224, 2019.Lagarias, J. C., Reeds, J. A., Wright, M. H., and Wright, P. E.: Convergence
properties of the Nelder–Mead simplex method in low dimensions, SIAM J.
Optim., 9, 112–147, 10.1137/S1052623496303470, 1998.Leonhardt, M., Kwiatek, G., Martínez-Garzòn, P., and Heikkinen, P.:
Earthquake catalog of induced seismicity recorded during and after
stimulation of Enhanced Geothermal System in Helsinki, Finland, GFZ Data
Services, 10.5880/GFZ.4.2.2021.001, 2021.Lomax, A.: A reanalysis of the hypocentral location and related observations
for the great 1906 California earthquake, B. Seismol.
Soc. Am., 95, 861–877, 10.1785/0120040141, 2005.Lund, B. and Townend, J.: Calculating horizontal stress orientations with
full or partial knowledge of the tectonic stress tensor, Geophys. J.
Int., 170, 1328–1335, 10.1111/j.1365-246X.2007.03468.x, 2007.
Majer, E., Nelson, J., Robertson-Tait, A., Savy, J., and Wong, I.: Protocol
for addressing induced seismicity associated with enhanced geothermal
systems, US Department of Energy, DOE EE-0662, 2012.Martínez-Garzón, P., Bohnhoff, M., Kwiatek, G., and Dresen, G.:
Stress tensor changes related to fluid injection at The Geysers geothermal
field, California, Geophys. Res. Lett., 40, 2596–2601,
10.1002/grl.50438, 2013.Martínez-Garzón, P., Kwiatek, G., Bohnhoff, M., and Ickrath, M.:
MSATSI: A MATLAB package for stress inversion combining solid classic
methodology, a new simplified user-handling, and a visualization tool,
Seismol. Res. Lett., 85, 896–904,
10.1785/0220130189, 2014.
Martínez-Garzón, P., Vavryčuk, V., Kwiatek, G., and Bohnhoff,
M.: Sensitivity of stress inversion of focal mechanisms to pore pressure
changes, Geophys. Res. Lett., 43, 8441–8450,
10.1002/2016GL070145, 2016.
Maxwell, S. C., Shemeta, J., Campbell, E., and Quirk, D.: Microseismic
deformation rate monitoring, in: SPE Annual Technical Conference and
Exhibition, 30 October–2 November 2011, Denver, Colorado, 4185–4193, 2008.Nelder, J. A. and Mead, R.: A simplex method for function minimization,
Computer J., 7, 308–313, 10.1093/comjnl/7.4.308, 1965.Rubinstein, J. L. and Ellsworth, W. L.: Precise estimation of repeating
earthquake moment: Example from Parkfield, California, B.
Seismol. Soc. Am., 100, 1952–1961,
10.1785/0120100007, 2010.Shelly, D. R., Hardebeck, J. L., Ellsworth, W. L., and Hill, D. P.: A new
strategy for earthquake focal mechanisms using waveform-correlation-derived
relative polarities and cluster analysis: Application to the 2014 Long
Valley Caldera earthquake swarm, J. Geophys. Res.-Solid
Ea., 121, 8622–8641, 10.1002/2016JB013437, 2016.Tape, W. and Tape, C.: Angle between principal axis triples, Geophys.
J. Int., 191, 813–831,
10.1111/j.1365-246X.2012.05658.x, 2012.Vavryčuk, V.: Principal earthquakes: Theory and observations from the
2008 West Bohemia swarm, Earth Planet. Sci. Lett., 305, 290–296,
10.1016/j.epsl.2011.03.002, 2011.Vavryčuk, V.: Iterative joint inversion for stress and fault
orientations from focal mechanisms, Geophys. J. Int., 199,
69–77, 10.1093/gji/ggu224, 2014.Waldhauser, F. and Ellsworth, W. L.: A double-difference earthquake location
algorithm: Method and application to the Northern Hayward Fault, California,
B. Seismol. Soc. Am., 90, 1353–1368,
10.1785/0120000006, 2000.Zhou, H.: Rapid three-dimensional hypocentral determination using a master
station method, J. Geophys. Res.-Solid Ea., 99,
15439–15455, 10.1029/94JB00934, 1994.