Journal of Geophysical Research: Solid Earth

Transcription

1 RESEARCH ARTICLE Key Points: Using ambient noise for reservoir monitoring Waveform decoherence related to well operations Observation of aseismic medium perturbations Correspondence to: A. Obermann, Citation: Obermann, A., T. Kraft, E. Larose, and S. Wiemer (2015), Potential of ambient seismic noise techniques to monitor the St. Gallen geothermal site (Switzerland), J. Geophys. Res. Solid Earth, 120, , doi:. Received 21 JAN 2015 Accepted 7 MAY 2015 Accepted article online 13 MAY 2015 Published online 8 Jun American Geophysical Union. All Rights Reserved. Potential of ambient seismic noise techniques to monitor the St. Gallen geothermal site (Switzerland) A. Obermann 1,T.Kraft 1, E. Larose 2, and S. Wiemer 1 1 Swiss Seismological Service, ETH Zurich, Zurich, Switzerland, 2 ISTerre, CNRS, Université Joseph Fourier, Grenoble, France Abstract The failures of two recent deep geothermal energy projects in Switzerland (Basel, 2006; St. Gallen, 2013) have again highlighted that one of the key challenges for the successful development and operation of deep underground heat exchangers is to control the risk of inducing potentially hazardous seismic events. In St. Gallen, after an injection test and two acid injections that were accompanied by a small number of micro-earthquakes (M L < 0.2), operators were surprised by an uncontrolled gas release from the formation (gas kick). The killing procedures that had to be initiated following standard drilling procedures led to a M L 3.5 earthquake. With ambient seismic noise cross correlations from nine stations, we observe a significant loss of waveform coherence that we can horizontally and vertically constrain to the injection location of the fluid. The loss of waveform coherence starts with the onset of the fluid injections 4 days prior to the gas kick. We interpret the loss of coherence as a local perturbation of the medium. We show how ambient seismic noise analysis can be used to assess the aseismic response of the subsurface to geomechanical well operations and how this method could have helped to recognize the unexpected reservoir dynamics at an earlier stage than the microseismic response alone, allowed. 1. Introduction The use of energy from deep geothermal reservoirs is gaining increasing attention as an alternative energy source [e.g., International Energy Agency, 2011]. To generate electricity, a geothermal resource requires fluid, heat and rock permeability. For an economical use, temperatures above 100 C and permeability rates exceeding 50 L/s need to be found at depth of at least 3 km in Europe [e.g., Giardini, 2009; Huenges and Ledru, 2010; Hirschberg et al., 2015]. To limit energy loss through long distance transport, proximity to the end users of heat and electricity is also desirable [Hirschberg et al., 2015]. There are two types of geothermal systems: 1. Hydrothermal systems: They ideally fulfill the requirements for economical use naturally. There is no need for a reservoir design as existent aquifers are targeted. They are mainly found at tectonic boundaries or volcanic areas [Barbier, 2002], and are, therefore, limited in their location and their potential for supplying electricity. 2. Enhanced (or engineered) geothermal system (EGS): The target area for EGS is the Earth s internally stored thermal energy in the crystalline basement that is typically characterized by low permeability. To create the required geothermal reservoir (volume >1km 3 )[Bachmann et al., 2011; Hirschberg et al., 2015], permeability needs to be enhanced. The permeability enhancement can be achieved by pumping high-pressure fluids into the rock mass (hydraulic stimulation), causing it to fracture [Smith, 1983; Tenzer, 2001; Giardini, 2009]. During the development and operation of such deep underground heat exchangers, injection tests or hydraulic stimulations cause ruptures that are detectable as micro-earthquakes (M L 2.0) by a sensitive seismic network [e.g.,wohlenberg and Keppler, 1987; Häring et al., 2008]. While the microseismicity can be beneficial, for instance, for the mapping of the EGS reservoir, a drawback is that the possible interaction of the pressurized fluid with existing faults is poorly understood, and the risk of producing felt or even damaging earthquakes cannot be ruled out completely, although it is statistically small [Giardini, 2009; Kraftetal., 2009]. As long as the risk is not under control, it limits the usability of EGS especially close to densely populated areas. The future development of deep geothermal energy, therefore, depends critically on the ability to engineer a lasting heat exchanger of economic size within deep, hot rocks and to assess and mitigate the potential risk posed by induced seismicity [e.g., Bachmann et al., 2011; Häring et al., 2008; Giardini, 2009; Gischig and Wiemer, 2013]. Research therefore focuses on improving established mitigation techniques for induced seismicity, e.g., traffic light systems [e.g., Bommer et al., 2006; Häring et al., 2008] in order to forecast seismic hazards during and after stimulation based on hydraulic data and observed seismicity [Mena et al., 2013; Goertz-Allmann and OBERMANN ET AL. MONITORING THE ST. GALLEN GEOTHERMAL SITE 4301

2 Wiemer, 2013; Ellsworth, 2013]. While efforts are concentrated on the seismic response of the underground on injection, aseismic processes which might be crucial for a better understanding of the stress evolution in the subsurface [Bourouis and Bernard, 2007] are not taken into account. In the last decade, three large geothermal energy projects were launched in Switzerland. The first one was the visionary EGS project within the city limits of Basel in 2006 [Häring et al., 2008]. In December 2006, a hydraulic stimulation of the crystalline basement at a depth of 5 km took place. The stimulation was accompanied by more than 10,500 earthquakes in the vicinity of the injection point in the first 6 days [Dyer et al., 2010]. These high rates of induced seismicity led operators to stop the stimulation after 6 of 21 originally planned days. A M L 3.4 earthquake occurred5haftershut-in, causing slight structural damages that lead to insurance claims exceeding U.S. $7 million [Kraft et al., 2009]. In the following 56 days three aftershocks of M L >3 followed [Häring et al., 2008] and resulted in the final closure of the project. In a recent study Hillers et al. [2015] observe an aseismic transient deformation induced by the 2006 Basel EGS stimulation with ambient seismic noise. The second project in the city of Zurich in 2010 did not yield the expected permeability rates and temperatures and is now used as a borehole heat exchanger ( The third large project close to the city of St. Gallen, in the eastern part of Switzerland, targeted preexisting faults in a Mesozoic limestone formation of the Molasse Basin (hydrothermal system). In mid-july 2013 a small-scale injection test took place followed by two stages of acid stimulation. A low level of microseismicity (M L < 0.2) was observed that correlated well with the hydraulic activities. Unexpectedly, gas then leaked into the well from an unknown source at depth; now speculated on as coming from a Permo-Carboniferous through underlying the Mesozoic sediments in the target area (M. Sonderegger, personal communication, 2014). The well was closed immediately, but the well head pressure continued to rise to levels that threatened to endanger the well integrity. To reduce the pressure at the well head the operators decided to pump drilling mud into the well. Seismicity increased within hours and triggered the yellow level of the traffic light system, but operators were obliged to continue this killing procedure to ensure the well integrity and drill site safety. The seismicity culminated in a maximum event of M L 3.5 on 20 July, 3:00 UTC. While the microseismicity at St. Gallen could be monitored down to levels of M L 1.1 [Edwardsetal., 2015], it only provided information on the seismic response of the underground. Yet aseismic responses to the injections that might give us important insights to the processes involved in deep-underground injections cannot be resolved with standard seismic analysis. Approaches of monitoring medium changes in the Earth often make use of measuring time-lapse changes in the traveltime of direct P and S waves [e.g., Lumley, 2001] and coda waves [e.g., Poupinet et al., 1984] from repeated events. For us, coda waves, the long-lasting scattered wave train in the later part of earthquake seismograms [Aki, 1969; Aki and Chouet, 1975], are of particular interest. Coda waves can last more than 10 times the traveltime of direct waves, before reaching the level of seismic background noise [Aki, 1969]. Due to scattering, coda waves spend a long time in the medium, sampling it repeatedly. The repeated sampling amplifies the waveform distortion from weak medium perturbations and allows tiny medium changes that might not be detectable in the first arrivals, to become detectable in the coda. In recent years, this sensitivity analysis has been increasingly used for monitoring purposes and became known as Coda Wave Interferometry [Poupinet et al., 1984; Snieder et al., 2002; Snieder, 2006]. For monitoring purposes a repeated energy source is required. Repeating earthquakes are difficult to use because of their uncontrollable timing and their uncertainty in source location. Controlled sources (explosives and vibroseismic) are very powerful, but the high operating costs prevent this technology from being used routinely. A powerful (and cheap) solution is offered by the omnipresent ambient seismic noise. For example, Claerbout [1968], Rickett and Claerbout [1996],Lobkis and Weaver [2001], Campillo and Paul [2003],Shapiro and Campillo [2004], and Wapenaar [2004] have shown that the cross-correlation function of a pair of recordings can be used to retrieve the Green s function between two receivers, as if one of the receivers behaved like an impulsive source. After these discoveries, surface waves from ambient seismic noise cross correlations have been widely used for high-resolution imaging of the Earth s lithosphere [e.g., Ritzwoller et al., 2011, and references therein]. Sens-Schönfelder and Wegler [2006], Wegler and Sens-Schönfelder [2007], and Brenguier et al. [2008b, 2008a] have demonstrated the feasibility to use noise cross correlations to continuously monitor changes in medium properties within volcanoes and active fault zones. With a novel inversion OBERMANN ET AL. MONITORING THE ST. GALLEN GEOTHERMAL SITE 4302

3 procedure, based on probabilistic approaches, Obermann et al. [2013a, 2014a] located such medium perturbations on volcanoes and fault zones in the horizontal plane. In this present paper, we demonstrate for the St. Gallen case that recent ambient seismic noise imaging techniques provide new possibilities to monitor reservoir changes beyond the purely seismic response. The ambient seismic noise monitoring enables us to observe the aseismic response of the reservoir to operator activities that remained undetected with the standard monitoring of microseismicity. In section 2 we describe the St. Gallen geothermal project and the seismic monitoring network. In section 3 we determine the temporal evolution of velocity and waveform coherence in the coda of the cross correlations. We observe a significant loss of waveform coherence parallel to the injection tests and prior to the M L 3.5 event. Next, we justify in detail why we believe that this observation is linked to a medium change and not an artifact of microseismicity or local source changes. We then estimate the spatial distribution of the change horizontally with an inversion procedure and vertically with help of a spectral analysis. The results show that the spatial distribution of the changes corresponds well with the injection location. In section 4 we evoke plausible scenarios that could explain our observations. 2. The St. Gallen Geothermal Project 2.1. Project Description The deep geothermal project in the vicinity of the city of St. Gallen, Switzerland, targeted a hydrothermal resource at a depth of km. The project was considered to be of low seismic risk, as hydraulic stimulation would only be needed if flow rates >50 L/s were not found. The project s target was the Mesozoic Malm and Muschelkalk layers of the Molasse sedimentary basin, which have already been successfully exploited in several deep geothermal projects in Southern Germany, that produce up to 150 L/s of 120 Â C hot water from 3.3 to 3.4 km depth [Agemar et al., 2014]. To improve the chances of finding high permeabilities, preexisting fault zones were targeted in most of these projects. The same procedure was chosen in St. Gallen. To find the optimal drilling target, a 3-D seismic survey was conducted in 2010, in the area covering 270 km 2. The survey revealed what was interpreted to be a pronounced fault zone, oriented NNE-SSW with a length of about 30 km and a width of about 100 m [Heuberger and Naef, 2012]. Heuberger and Naef [2012] concluded that the faults were ideally oriented in the stress field to be reactivated. Further, they suggested that this orientation indicated that the fault zone might exhibit an increased permeability. An analysis of the historically and instrumentally recorded seismicity ( ) shows, with the exception of one historical event with M w 4.7 in 1835 in Abtwil [Diehl et al., 2014], a spatially diffuse seismicity with events M L < 3 that cannot be attributed to the fault zone [Diehl et al., 2014]. Despite its optimal orientation in the present stress field, the operator interpreted the fault to be hardly seismically active. In the first half of 2013 an injection well was drilled to a depth of 4500 m. Within the 400 m long open-hole section, temperature logs had indicated the presence of at least two high-temperature fracture zones. To gain a better understanding of the reservoir, a small-scale injectivity test was performed on 14 July Within 2 h, a total of 175 m 3 of cold water was injected into the roughly 400 m long open-hole section of the well. During the test, the flow rate was gradually increased up to a maximum of 3100 L/min. The downhole pressure increased by 7.5 MPa to 44.5 MPa compared to the initial pressure at a depth of 3989 m. A total of 12 micro-earthquakes were detected with a maximum magnitude of M L 0.1 (Figure 1). On 17 July, two acid stimulations were performed, each injecting 145 m 3 of diluted hydrochloric acid into the reservoir at flow rates of up to 2500 L/min and well head pressures of up to 8 MPa. The seismicity (roughly 40 events on 17 and 18 July) associated with these tests did not exceed magnitude M L 0.2 (Figure 1) and was judged to be well within the expected range. On 19 July around noon, gas (90% methane) unexpectedly entered the borehole from an unknown source at depth. The sudden gas release hit the operators at the worst possible moment; the drill string was only 200 m inside the well, and the well was open due to the preparation of a production test. The borehole was closed immediately. However, with the rising gas, pressure at the well head rose, reaching 9 MPa, when operators decided to take measures to fight the gas kick and secure the well. Over 18 h, a total of about 700 m 3 of drilling mud was pumped into the well to push the gas back into the formation, successfully leading to a steady decrease of the pressure at the well head. This well control procedure was accompanied by a sequence of induced earthquakes (Figure 1). At 7 P.M. on 19 July, once half of the total volume had been injected and well head pressures had decreased to 2.5 MPa, the seismicity intensified. An event with magnitude M L 1.6 triggered the yellow threshold of the traffic light system in operation. A yellow alarm requires the pumps OBERMANN ET AL. MONITORING THE ST. GALLEN GEOTHERMAL SITE 4303

4 Figure 1. Seismicity measured in St. Gallen from 13 July until the end of October The magnitudes M L are taken from Edwards et al. [2015]. The completeness limit indicates the smallest magnitude above which all earthquakes in the seismogenic volume have been detected assuming that the magnitude-frequency distribution follows the Gutenberg-Richter law [e.g., Mignan and Woessner, 2012]. Furthermore, the detection limit indicates the magnitude of the smallest earthquakes detected during the daytime on working days. to be stopped. However, due to the ongoing well control operation, stopping the pumps would likely have caused a renewed increase in the gas content and well head pressure, possibly up to levels endangering well integrity. Operators therefore decided to continue pumping. On 20 July at 5:30 A.M. local time, the largest event of the sequence occurred, with a magnitude of M L 3.5 (M w 3.3). Retrospectively, Diehl et al. [2014] performed a relative relocation of the earthquake sequence and showed that a lineament of about 500 m length of the targeted fault zone, was highlighted with seismicity during the acid stimulation. They also showed that the seismicity remained constrained to within m epicentral distance of the open-hole section of the borehole [Diehl et al., 2014]. After the main shock, roughly 800 aftershocks were detected (M L 1.1 to 2.4) particularly during a period of weak steady mud losses (15 September to 15 October) that ceased after a production test in October (15 October to 3 November). Using this case, we show how an ambient seismic noise analysis can be used to assess the aseismic response of the subsurface to well operations and how this method could have helped to recognize the unexpected process much earlier than the microseismic response allowed Seismic Monitoring Network Within the GEOBEST project ( index), the Swiss Seismological Service operated a seismic monitoring network (Figure 2) for the St. Gallen geothermal project. The network consisted of five broadband stations (Nanometrics Trillium compact; SGT01 SGT05; red triangles in Figure 2), one 4.5 Hz borehole sensor of type IESE-S21g at 205 m depth (SGT00; OBERMANN ET AL. MONITORING THE ST. GALLEN GEOTHERMAL SITE 4304

5 Figure 2. Seismic stations in St. Gallen used in this study: five 120 s broadband stations (SGT01 SGT05), one 4.5 Hz borehole geophone (SGT00) and three 1 Hz short-period sensors (SGT06, SGT07, and SGT09). blue circle in Figure 2), and three short-period sensors of type Lennartz LE3D-1Hz (SGT06, SGT07, and SGT09; green inverted triangles in Figure 2). In section we take a closer look at the noise levels of SGT00 and SGT01. The short black line in Figure 2) marks the horizontally projected position of the injection well. The broadband stations and the borehole sensor recorded data from February 2012 until at least the end of 2013, while the short-period stations started recording only prior to the injection tests in July As there were data quality issues with some of the broadband stations in spring 2012, we only consider data from August 2012 onward. Figure 3 shows the data availability for all stations during the period of interest. 3. Monitoring the St. Gallen Project With Ambient Noise Correlations 3.1. Computation of the Cross-Correlation Functions We analyze the vertical component of the continuous noise records and apply preprocessing steps that are slightly modified from Sabraetal.[2005] and Bensen et al. [2007] prior to the calculation of the cross correlations. As we have different sensors SGT09 SGT07 SGT06 SGT05 SGT04 SGT03 SGT02 SGT01 SGT00 Oct Feb May Aug Nov Figure 3. Data availability for all the stations during the period of interest. The vertical blue lines mark the injection activities and the M L 3.5 earthquake. within the network, we first remove the instrument response. We then apply a band-pass filter ( Hz) and time domain clipping (removal of time windows with high amplitudes) to remove major high-frequency spikes and glitches. We downsample the data to 20 Hz and subdivide each daily record into twelve 2 h long segments. In each of these time segments, we look for instrumental issues or transient signals. If the energy in a segment is greater than 1.5 times the average energy of the 1 day trace, we remove the 2 h segment (we do not correlate it). We then whiten the spectral domain in a relatively large frequency band of Hz. OBERMANN ET AL. MONITORING THE ST. GALLEN GEOTHERMAL SITE 4305

6 Figure 4. (a) Acausal and causal part of the cross-correlation function for station pair SGT04-SGT05 for the 17 months of interest. (b) Symmetrized cross-correlation function for station pair SGT04-SGT05, (c) SGT00-SGT05, and (d) SGT01-SGT05. The horizontal black line marks the M L 3.5 event. The vertical white dashed lines mark the time window used for the stretching. As a final step, we apply a 1 bit amplitude normalization [Larose et al., 2004]. We then calculate the noise cross-correlation functions between all station pairs ij for each 2 h segments and stack the existing segments to 1 day correlation functions φ ij (at least six, otherwise the day is rejected). An example of the computed cross-correlation functions after processing is shown for the station pair SGT04-SGT05 in Figure 4a. This station pair is relatively noisy but a coherent signal can still be seen until about 50 s in the coda. This cross-correlation function, as well as the others, is asymmetric due to the anisotropy of the noise propagation directions [Stehly et al., 2007]. We retain the symmetric part by averaging the positive and negative lag times [Mordret et al., 2014], as shown in Figures 4b 4d for the station pairs SGT04-SGT05, SGT00-SGT05, and SGT01-SGT Temporal Variations in the Coda To quantify the temporal variations in the coda of the correlations, we use the stretching technique [Lobkis and Weaver, 2003; Sens-Schönfelder and Wegler, 2006]. We compare, for each receiver pair ij, a5daylong current stack φ curr = φ d d+5 around day d to a reference stack φ ref that is the average over all available daily ij ij ij cross-correlation functions. The method is based on the concept that a spatially homogeneous velocity variation (in contrast to a local velocity variation) in the medium will result in a stretching or compression of the time axis by a factor t t(1 ε) when compared to the reference [Lobkis and Weaver, 2003; Sens-Schönfelder OBERMANN ET AL. MONITORING THE ST. GALLEN GEOTHERMAL SITE 4306

7 Figure 5. (a) Apparent velocity changes ε and (b) waveform coherence CC. The thick blue lines show the averages over all station pairs ij. The vertical lines mark the injection tests and the M L 3.5 earthquake. and Wegler, 2006]. The optimal coefficient ε maximizes the coherence CC(ε) between the current stretched signal φ curr and the reference signal φ ref [Hadziioannou et al., 2009]: ij ij CC(ε) = t 2 φ curr [t(1 ε)] φ ref [t]dt t 1. (1) t 2(φ t curr ) 2 [t(1 ε)] dt t 2(φ 1 t ref ) 2 [t]dt 1 In the following the dimensionless coefficient ε = δv = δt is referred to as apparent velocity change and v t CC = max(cc(ε)) as waveform coherence. In the case of local medium perturbation, we do not expect a linearly increasing velocity change with increasing time in the coda [Obermann et al., 2013b]. Therefore, we apply the stretching technique to short time windows ( 5 periods of the signal) in the coda. In this section, we present time-lapse results (for the Hz data) using a time window of 20 s centered around 15 s in the coda, as depicted in Figure 4b. In Figure 5a we display the apparent velocity variations ε for all station pairs ij from August 2012 until the end of The thick blue line is the average over all station pairs ij. The vertical black lines mark the injection test on 14 July 2013, the acid injections on 17 July 2013 and the M L 3.5 earthquake on 20 July In Figure 5b we display the waveform coherence (CC) for all station pairs ij for the same period. Again, the thick blue line is the average from station pairs ij. The waveform coherence is very high with an average of 0.9 during periods with no operator activity (August 2012 until end of June 2013), indicating a high resemblance between the correlation functions from 1 day to the other. During the same period, we can observe fluctuations in the order of 10 20% on individual station pairs that indicate the confidence level for each pair. A longer current stack φ curr (e.g., 10 or 20 days) would ij have stabilized the cross-correlation functions further and decreased the degree of fluctuations. Nevertheless, as we are interested in a high temporal resolution, we opted for a relatively short stack of 5 days. During the period of injection activity, we observe a significant decrease of coherence (on average down to 0.55) that is seen by several station pairs at the geothermal site. After roughly 30 days, the coherence recovered its original values. As we explain in the following, we are confident that this loss of coherence is related to the injected fluid volumes. OBERMANN ET AL. MONITORING THE ST. GALLEN GEOTHERMAL SITE 4307

8 Figure 6. Observed waveform coherence (CC) for the indicated station pairs: (a) close to the injections and (b) further away from the injections. The vertical lines in the CC plots mark the injection tests, the gas kick and the M L 3.5 earthquake. The apparent velocity changes (ε) (Figure 5a) show fluctuations of ±0.01%around the mean of 0 during the entire observation period. There is no clear trend during the injection period. This lack of a clear change in velocity was unexpected for us, as pressure changes in the subsurface often lead to observable velocity perturbations [e.g., Wegler and Sens-Schönfelder, 2007; Brenguier et al., 2008b; Obermann et al., 2013a]. We will come back to the lack of a clear change in velocity when we interpret our findings in section 4. In Figure 6 we have a closer look at the loss of coherence during the period of injection activity in July 2013 (beginning of July to the end of August 2013). We study two different subsets of exemplary station pairs. In Figure 6a we study station pairs with trajectories that cross the injection area (as indicated in the map on the left of Figure 6a), while in Figure 6b we study station pairs with trajectories that do not cross the injection area (as indicated in the map on the left of Figure 6b). The solid gray lines mark the injection test (175 m 3 ) and the acid treatments (290 m 3 ). The light gray line indicates the earliest possible influence of the injection on the displayed data due to the 5 days stack used in the stretching procedure. The yellow line marks the gas kick, and the solid red line the M L 3.5 earthquake on 20 July. The thin red line marks the end of the influence of the earthquake on the displayed data due to the 5 days stack. The station pairs at a distance from the stimulation tests (Figure 6b) do not show a significant loss of coherence in July 2013, whereas we notice a clear loss of coherence for all displayed station pairs that are in the vicinity of the injections (Figure 6a). The coherence loss is strongest on 19 and 20 July 2013 with the gas kick and the M L 3.5 earthquake. For all displayed station pairs we notice clearly that the distortion of the waveform starts with the onset of the influence of the first injection (14 July), prior to the earthquake Arguments for the Loss of Coherence Due To Medium Changes A loss of waveform coherence can mean one of two things; the Green s function is not recovered (which is not useful) or the scattering properties have changed in the medium, so that the Green s function has been altered [Larose et al., 2010; Obermann et al., 2013a, 2014b; Planès et al., 2014]. In the present study we observe OBERMANN ET AL. MONITORING THE ST. GALLEN GEOTHERMAL SITE 4308

9 octave wide bp. velocity [m/s] 1e-3 1e-4 1e-5 1e-6 1e-7 1e-8 1e-9 Brune source: stress drop 2.1 MPa (Edwards et al., 2014) attenuation (Edwards et al., 2011) distance 5000m octave wide bp. velocity [m/s] 1e-3 1e-4 1e-5 1e-6 1e-7 1e-8 1e-9 Brune source: stress drop 2.1 MPa (Edwards et al., 2014) attenuation (Edwards et al., 2011) distance 5000m 1e-10 1e-2 1e-1 1e+0 1e+1 1e+2 frequency [Hz] 1e-10 1e-2 1e-1 1e+0 1e+1 1e+2 frequency [Hz] Figure 7. Comparison of theoretical Brune S wave source spectra (red) and ambient seismic noise levels (blue) at station (left) SGT00 and (right) SGT01. Amplitudes are given in octave band-passed velocity in m/s. The mode of the station noise levels of the vertical component (solid blue) and its 5 and 95 percentiles (dashed blue) are shown. The U.S. Geological Survey (USGS) low and high noise model [Peterson, 1993] are indicated in green. For the measurements we used a frequency band of Hz, and we see the strongest changes at frequencies of Hz. very strong losses of coherence (up to 100%) for some station couples and hence present arguments why we still interpret the observed decoherence as being related to medium changes. 1. The preprocessing steps. As described in section 3.1, we remove all 2 h segment of the noise records in which we detect signals with 1.5 times the average energy of the daily trace. Micro-earthquakes with less energy might not be removed by this procedure, but they have durations of a few seconds only and will hence not have an impact on the stacked daily cross correlations. The 1 bit amplitude normalization that we apply also helps to minimize the effect of higher-energy signals. 2. The frequency spectrum of the micro-earthquakes. In Figure 7 we show the noise levels measured at stations SGT00 (short period, borehole) and SGT01 (broadband, surface). The noise levels were calculated using the software PQLX [McNamara and Boaz, 2006], which computes probability density functions (pdf) of the power spectral density (psd) of the ground motion observed at a station (see McNamara and Boaz, 2006 for details). From the pdf for the whole installation period of a station, we derived the statistical moments mode, 5 and 95 percentiles to represent the most probable noise level at a station and its variation in the frequency range of 0.01 Hz to 100 Hz. We compare the station noise levels to synthetic S wave source spectra for earthquakes between magnitude M L 1.5 to M L 3.5. The source spectra are calculated assuming a Brune source [Brune, 1970, 1971] with a stress drop of 2.1 MPa (derived by Edwardsetal.[2015] for the M L 3.5 event of the St. Gallen sequence), the Swiss attenuation model of Edwards et al. [2011], and a hypocentral distance of 5 km. Further, we assume a uniform radiation pattern using the RMS average over the whole focal sphere of a double-couple source (0.63) [Aki and Richards, 1980]. For easier comparison we convert psd amplitudes to octave band-passed velocity [e.g., Bormann, 1998; Clinton and Heaton, 2002]. As can be derived from Figure 7, in the frequency band of interest in this study ( Hz), the ambient noise level at the stations SGT00 and SGT01 is higher than the expected signal amplitude of earthquakes with magnitudes smaller or equal M L 0.5. Therefore, only earthquakes with larger magnitudes may have an influence on the ambient seismic noise analysis at these stations. As the noise level of the two stations shown is representative for the whole St. Gallen network, the conclusion is also true for all other stations used in this study, which have larger hypocentral distances than SGT00 and SGT01, and, therefore, smaller signal amplitudes for earthquakes of the same magnitude. In Figure 6a we can see that the loss of coherence starts with the first injection test on 14 July. This test is accompanied by 12 microseisms with M L < 0.1 (see section 2.1). The potential noise induced by these events is below the noise level of the frequency range of interest (0.1 1 Hz). The same applies for the 40 microseisms with M L < 0.2 that occurred during the acid treatments. From 19 July onward, we had around 20 events of M L > 0.5 that are within the noise level of frequencies f >0.6 Hz and hence lie within the large-frequency band considered in this section. They might have added to the loss of coherence that we observe in Figure 6a after 19 July. To anticipate the results of the frequency analysis in section 3.4, we see the strongest loss of coherence for frequencies between 0.2 and 0.4 Hz. This frequency band might only be OBERMANN ET AL. MONITORING THE ST. GALLEN GEOTHERMAL SITE 4309

10 Figure 8. (left) Ambient seismic noise amplitude spectrogram for the stations SGT01, SGT02, and SGT03 between 0.1 and 1 Hz from August 2012 until October (right) Smoothened amplitudes averaged over a Hz frequency band for each station, respectively. The red lines mark the injection on 14 July influenced by events of M L > 1.5. We have had a total of four events with M L > 1.5 occurring on 19 and 24 July, but none during the injection phase. 3. Unchanged local source spectrum. To illustrate the observed waveform decoherence is not related to any change in source of the local noise field, we compare the seismic noise observed at different stations in our study area. In Figure 8 (left) we show spectrograms of the power spectral density for frequencies ranging from 0.1 to 1.0 Hz for the stations SGT01, SGT02, and SGT03. All three stations are located in considerable distance to the drill site (SGT01: 2 km; SGT02: 10 km; and SGT03: 12 km). In Figure 8 (right) we plot the corresponding amplitudes averaged over a frequency band of Hz, which corresponds to the frequencies where we found the strongest loss of coherence (see section 3.4). The vertical red lines mark the first injection on 14 July The figure shows no significant differences in the variation of the observed ground motions at any station for the entire study period. Therefore, we conclude that no significant changes in the local noise field between the stations occurred in this period. The frequency band of interest is therefore not impacted by the onset of the well operations in July This allows us to rule out human activity as a source for the changes at periods above 1 s, an observation that is also confirmed by a detailed characterization of ambient noise near the geothermal sites in northern Alsace from Lehujeur et al. [2015]. We therefore interpret the observed loss of coherence prior to the earthquake as a medium change, potentially triggered by the injection activity. Before we go further with the interpretation, we determine the location of the medium changes in the horizontal (section 3.3) and vertical plane (section 3.4) Inversion to Constrain the Observed Medium Perturbations Horizontally Inversion Procedure As we could see, the measured waveform coherence CC depends on the position of the stations relative to the change in the medium. We now use this spatial dependency to constrain the extent of the medium changes in the horizontal plane with an inversion procedure based on probabilistic approaches [Obermann et al., 2013a, 2014a]. Therefore, we relate CC to a local medium perturbation in x 0 using the sensitivity kernel introduced by Pacheco and Snieder [2005],Larose et al. [2010], and Planès [2013]: K(s 1, s 2, x 0, t) = t 0 p(s 1, x 0, u)p(x 0, s 2, t u)du p(s 1, s 2, t) (2) OBERMANN ET AL. MONITORING THE ST. GALLEN GEOTHERMAL SITE 4310

11 where s 1 and s 2 are the positions of the stations, x 0 is the position of the medium perturbation and t is the center of the time interval in the coda where the stretching is evaluated. p(s 1, s 2, t) is the intensity propagated from s 1 to s 2 at time t, which can be approximated by the intensity of the wave field from s 1 to s 2 at time t. The integration variable, u, is the propagation time from the source to the medium perturbation x 0.Asan intensity propagator we use the solution of the radiative transfer equation or Boltzmann transport equation [Chandrasekhar, 1960]. Since surface waves are the dominant wave type, we use the analytic 2-D solution of the radiative transfer for isotropic scattering [Shang and Gao, 1988; Sato, 1993; Paasschens, 1997]: ( ) 1 [ ] p(r, t) = e ct l 1 δ(ct r)+ 1 r2 2 l exp 1 ( c 2 t 2 r 2 ct) Θ(ct r) (3) 2πr 2πlct c 2 t 2 where c is the wave speed, r is the distance between source and receiver, l is the transport mean free path, and Θ(x) is the Heaviside (or step) function. The first term describes the coherent part of the intensity that decreases exponentially with the distance relative to the transport mean free path. The second term describes all other orders of scattering (diffuse intensity). For the computation of the sensitivity kernels, we use the following parameters: 1. c = 1.1 km/s; the Rayleigh wave group velocity in the frequency band of Hz. 2. t = 15 s; the center of the time window used for the stretching (see section 3.2). 3. l = 100 km; the transport mean free path is not known for St. Gallen. We approximate this quantity based on values that we found in the literature for similar frequency bands and geological settings. DelPezzoetal. [2001] found l = 40 km at Mount Etna volcano, a medium that is probably more heterogeneous than the fractured crust in St. Gallen and Przybilla et al. [2009] determined values of about l = 200 km with a numerical simulation for the crust that is probably not heterogeneous enough. We hence based our estimate in between with l = 100 km. As Obermann et al. [2013a] have shown, the main characteristics of the inversion result do not change with the different choices of the transport mean free path. To estimate the horizontal distribution of the medium changes, we have the system of linear equations in matrix form according to d = Gm (4) where d is a vector that contains the coherence measurements between all station pairs. G is a matrix, for which each component G ijx = cδs K 2 ij x corresponds to the sensitivity kernel K for station pair ij in cell x evaluated at time t in the coda and weighted by the surface area of the cells Δs and the Rayleigh wave group velocity c. m is a vector that contains the scattering cross-section density change σ d (km/km 2 ) that we estimate for each pixel j. We use a formulation of the least squares method for linear problems as proposed by Tarantola and Valette [1982] to determine m. For more details on the inversion, please refer to Obermann et al. [2013a, 2014a] Inversion Results We invert the changes that occurred, averaged over the month of July relative to the rest of the observation period ( ). The result is displayed in Figure 9 as a map of the scattering cross-section density σ that is associated with the strength and size of the medium change. The changes that we observe are spatially constrained within 3 6 km of the injection well suggesting that they are related to the injection activities. The maximum of the changes is northwest of the injection well, which might be slightly biased by the network setup Spectral Analysis to Constrain the Depth of the Changes To gain a more precise idea about the depth of the changes, we perform a spectral analysis. Following the assumption that the coda waves are dominated by Rayleigh waves several studies have used the frequency-dependent depth sensitivity of Rayleigh waves to estimate the depth of velocity changes [Rivet et al., 2011; Mainsant et al., 2012; Hobiger et al., 2012]. We apply this concept here for the first time to the waveform coherence. We concentrate on the station pairs SGT00-SGT04 and SGT01-SGT04 that cross the area of change as identified in Figure 9. We recompute the cross correlations in narrow bands of ±10% around central frequencies f 0 ranging from 0.07 to 1 Hz. For each of these frequencies, we determine the waveform coherence with the stretching technique in a time window that becomes successively larger for lower frequencies (at least five periods long). We then OBERMANN ET AL. MONITORING THE ST. GALLEN GEOTHERMAL SITE 4311

12 Figure 9. Scattering cross-section density changes derived by least squares inversion averaged over July The observed changes are around the injection well, indicating a causal relationship with the activities at the well. average the coherence values for the selected station pairs over the month of July and plot them in Figure 10 versus f 0. We observe that the loss of coherence is the strongest for frequencies between 0.2 and 0.4 Hz, while there is no decoherence for frequencies higher than 0.6 Hz. To evaluate the depth sensitivity of the Rayleigh waves at these frequencies, we compute the corresponding depth sensitivity kernels. As the 3-D seismic data are at present not open for public access, we use a 1-D velocity profile of the St. Gallen area (Figure 11a) that was provided by Diehl et al. [2014]. For the computation of the kernels, we use the open source software developed by Herrmann and Ammon ( The resultant depth sensitivity kernels are represented in Figure 11b in the form of the partial derivatives. The Figure 10. Waveform coherence averaged over the month of July 2013 computed at different frequencies for the two station pairs crossing the medium change (red, blue). target layer of the injection (Malm) is marked in gray. We see that only Rayleigh waves at 0.2, 0.3, and 0.4 Hz are sensitive to the target layer (around 4 km depth), while higher frequencies (0.5 2 Hz) are sensitive to the shallower subsurface (0 4 km) and lower frequencies (<0.2 Hz) are sensitive to depths >6 km. As we have seen in Figure 10, the loss of coherence is the strongest for frequencies between 0.2 and 0.4 Hz, while other frequencies (lower and higher) do not show a significant loss of coherence. This allows us to restrain the changes to the regions that coincides with the hydraulic stimulation. OBERMANN ET AL. MONITORING THE ST. GALLEN GEOTHERMAL SITE 4312

13 Figure 11. (a) Minimum 1-D velocity model V S (red) and V P (blue) for St. Gallen [Diehl et al., 2014]. The gray region marks the target Malm layer where the hydraulic test and chemical stimulation took place. (b) Rayleigh wave depth-sensitivity kernels computed at different frequencies for the velocity model displayed in Figure 11a. We here computed the sensitivity of the Rayleigh phase velocity c, to a shear velocity perturbation dv S. 4. Interpretation and Conclusion We observe a significant loss of waveform coherence in the seismic coda that starts with the injection of small amounts of water and acid (465 m 3 ) into the well. The loss of coherence reaches its maximum (with a total loss of coherence observed at some station pairs) at the time of the gas kick or the M L 3.5 earthquake, depending on the region sampled by the station pairs. Roughly 30 days after the M L 3.5 event, the coherence on all station couples recovers to the initial values. Other operator activity, such as the production test on 19 October, did not cause any further decoherence of the waveforms. We are certain that the loss of coherence is due to a perturbation in the subsurface. We can spatially constrain this perturbation within a few kilometers around the injection interval, both horizontally and vertically. From the data of 17 months that we used for our study, we did not observe any significant change of apparent velocity ε. The microseismicity during the injections was well within the expected range with approximately 50 events of M L < 0.2. The lack of a significant apparent velocity change ε during the injection activities is likely due to the small size and depth of the postulated medium perturbations. Applied to numerical simulations, Planès et al. [2015] have shown the capacity of waveform coherence studies to detect and locate multiple structural changes with sizes of λ/5 (where λ is the wavelength of the surface waves) in a highly heterogeneous medium. Studies focusing on velocity changes (phase shifts) have so far only detected and located changes when they were larger than λ [Brenguier et al., 2008b; Duputel et al., 2009; Obermann et al., 2013a, 2014b; Hillers et al., 2015]. In the present study, we observe the maximum waveform perturbations for lower frequencies ( Hz). The wavelength for these frequency bands is about 7 15 km, while we expect the medium changes to only extend over 3 6 km (Figure 9). To explain the decoherence, we speculate about three possible scenarios listed below. The first two scenarios strongly implicate the presence of gas, while the third scenario only depends on the presence of a critically prestressed fault. Without extensive 3-D simulations (that are beyond the scope of this paper), we cannot more precisely determine the role of the gas for the seismic observations linked to the geothermal project in St. Gallen. It is important for all scenarios that even tiny pore pressure changes can be transported quickly over large distances along fractures and faults with enhanced permeability [Zoback, 2010]. OBERMANN ET AL. MONITORING THE ST. GALLEN GEOTHERMAL SITE 4313

14 1. Changes in attenuation and/or reflectivity due to the gas. Methane gas hydrate can have a dramatic effect on seismic wave attenuation [Priest et al., 2006] and reflectivity [Ghazali, 2011]. Shallow gas accumulations are recognized as strong reflection amplitude anomalies due to the large acoustic impedance contrast between gas-filled and sand-silt-filled media. This could explain the strength of the decoherence as well as its return to the initial values once the gas is back to its original formation and the possibly remaining gas concentration dispersed. 2. Critically prestressed fault. The drilling targeted a fracture zone that extends several kilometers to the North and South and has a width of possibly 100 m [Heuberger and Naef, 2012]. As indicated by Heuberger and Naef [2012] this fault zone is optimally oriented in the present-day stress field. The fault was therefore most likely critically prestressed and even small pore pressure increases could initiate local slip on fault patches. These pore pressure changes may then have caused perturbations of the seismic waveforms. 3. Pore pressure changes due to injected water or released gas. Pore pressure changes could be induced either by the injected water volume or by the released gas. Since the 1950s hydrocarbon exploration has been conducted in the Alpine foreland [Huber et al., 1990]. Discoveries were made predominantly in the early Tertiary sediments [Huber et al., 1990], but in the western Foreland Molasse, Mesozoic beds are also productive [Lemcke, 1979]. The gas often occurs in highly overpressured pockets, rendering hydrocarbon exploration in the Alpine thrust belt a special challenge as pore pressure may be very close to fracturing pressure [Huber et al., 1990; Müller and Nieberding, 1995]. From this experience, gas in St. Gallen was mainly expected in much shallower formations than the targeted Mesozoic Malm and Muschelkalk layers. The Permo-Carboniferous Through (PCT), from which the gas in St. Gallen most likely originated, had not been clearly identified on the 3-D seismic survey. It is possible that the pore pressure change induced by the injections interacted with the overpressured PCT, resulting in a significant stress change that acted on the numerous faults and fault patches within the fracture zone and caused a significant, locally constrained, perturbation of the seismic waveforms. In conclusion, it was likely a combination of the described effects that caused the remarkable loss of coherence that we observe during the injection period in St. Gallen. We are convinced that ambient noise correlations offer interesting possibilities to assist the monitoring of engineering projects involving subsurface stimulations. Particularly when applied to denser networks, ambient noise monitoring techniques could also help to better understand reservoir dynamics and mitigate associated risks. Acknowledgments We would like to thank the Sankt Galler Stadtwerke for access to data and information. The research leading to these results has received funding from the European Community s Seventh Framework Programme under grant agreement (Project IMAGE) and the Swiss Federal office of Energy with the project GEOBEST. To request seismological waveform data used in this study, please contact the Swiss Seismological Service. References Agemar,T., J.Weber, and R. Schulz (2014), Deep geothermal energy production in Germany, Energies, 7, Aki, K. (1969), Analysis of seismic coda of local earthquake as scattered waves, J. Geophys. Res, 74, Aki, K., and B. Chouet (1975), Origin of coda waves: Source, attenuation and scattering effects, J. Geophys. Res, 80, Aki, K., and P. 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