Microseismicity Associated with Actively Exploited Geothermal Systems: Earthquake Detection and Probabilistic Location at Rotokawa and Statistical Seismic Network Design at Kawerau

<p>Geothermal power has progressively been recognised as an important energy resource due to the depletion of old power sources, and as a more environmentally aware population pushes for an increase in renewable energy sources. Monitoring microseismicity occurring in active geothermal systems is one means of both characterising the system’s fault architecture and characterising fluid/rock interaction in response to production. This study focuses on better understanding seismicity in two active geothermal fields, through the development and implementation of two different algorithms: an automated microearthquake detection algorithm using a matched filter technique (improving earthquake detection), and an optimal seismic network design algorithm (improving earthquake location). Both algorithms have been implemented in codes that are easily adaptable to other data sets. The first of these algorithms has been applied to five months of continuous seismic waveform data spanning a fluid injection operation in the Rotokawa geothermal field. The cross-correlation of 14 high-quality master events with the continuous seismic data yields 2461 newly detected earthquakes spanning the magnitude range M=-0.4 to M=2.6 with a mean magnitude of M=0.47. The earthquakes detected with each master event exhibit high waveform similarity over approximately three orders of magnitude, and appear to follow a Gutenberg-Richter power law with a catalogue completeness down to M~ 0. Hypocentres for these detected events computed using the probabilistic earthquake location algorithm NonLinLoc reveal the dominant locus of seismicity to lie between 1.0–2.5 km depth, a location consistent with that of the Rotokawa Andesite which forms the Rotokawa reservoir. Focal mechanism solutions for the master events are predominantly normal, with half displaying a large strike-slip component, and the stress parameters obtained for this suite of focal mechanisms imply a northeast–southwest oriented maximum horizontal stress: both of these results are consistent with the extensional regime of the TVZ. Seismicity occurring within a 300 m horizontal radius of the injection well’s feed-zones, and extending to 5 km depth, initially exhibits a correlation with injection flow rates with a ~ 2 day lag, and seismicity rates decrease ~ 10 weeks after injection. We surmise that seismicity within the injection region and close to the injection well is likely to be injection-induced, with one portion of the injectate returning to the production region, while the other either migrates southeastward out of the field or remains within the injection region; the origin of seismicity within the production region in relationship to production and injection processes is unclear. The second of these algorithms involves the derivation of a design criterion, which we apply to inform the expansion of the existing seismic monitoring programme at Kawerau geothermal field; we also apply an early version to the short-term/rapid-response network design following the M7.1 September 2010 Darfield earthquake. Unlike previous seismic network design algorithms, the new algorithm incorporates methods for the realistic representation of 3D velocity structures and attenuation models for both P and S travel times, a surface noise model, and the ability to apply complex weighting functions to the earthquake set. The results demonstrate the utility of this algorithm in even simplistic cases, and show how each new parameter incorporated into the design model affects the optimal network design obtained, identifying the need for accurate input data to provide optimal results.</p>

Microseismicity Associated with Actively Exploited Geothermal Systems: Earthquake Detection and Probabilistic Location at Rotokawa and Statistical Seismic Network Design at Kawerau

<p>Geothermal power has progressively been recognised as an important energy resource due to the depletion of old power sources, and as a more environmentally aware population pushes for an increase in renewable energy sources. Monitoring microseismicity occurring in active geothermal systems is one means of both characterising the system’s fault architecture and characterising fluid/rock interaction in response to production. This study focuses on better understanding seismicity in two active geothermal fields, through the development and implementation of two different algorithms: an automated microearthquake detection algorithm using a matched filter technique (improving earthquake detection), and an optimal seismic network design algorithm (improving earthquake location). Both algorithms have been implemented in codes that are easily adaptable to other data sets. The first of these algorithms has been applied to five months of continuous seismic waveform data spanning a fluid injection operation in the Rotokawa geothermal field. The cross-correlation of 14 high-quality master events with the continuous seismic data yields 2461 newly detected earthquakes spanning the magnitude range M=-0.4 to M=2.6 with a mean magnitude of M=0.47. The earthquakes detected with each master event exhibit high waveform similarity over approximately three orders of magnitude, and appear to follow a Gutenberg-Richter power law with a catalogue completeness down to M~ 0. Hypocentres for these detected events computed using the probabilistic earthquake location algorithm NonLinLoc reveal the dominant locus of seismicity to lie between 1.0–2.5 km depth, a location consistent with that of the Rotokawa Andesite which forms the Rotokawa reservoir. Focal mechanism solutions for the master events are predominantly normal, with half displaying a large strike-slip component, and the stress parameters obtained for this suite of focal mechanisms imply a northeast–southwest oriented maximum horizontal stress: both of these results are consistent with the extensional regime of the TVZ. Seismicity occurring within a 300 m horizontal radius of the injection well’s feed-zones, and extending to 5 km depth, initially exhibits a correlation with injection flow rates with a ~ 2 day lag, and seismicity rates decrease ~ 10 weeks after injection. We surmise that seismicity within the injection region and close to the injection well is likely to be injection-induced, with one portion of the injectate returning to the production region, while the other either migrates southeastward out of the field or remains within the injection region; the origin of seismicity within the production region in relationship to production and injection processes is unclear. The second of these algorithms involves the derivation of a design criterion, which we apply to inform the expansion of the existing seismic monitoring programme at Kawerau geothermal field; we also apply an early version to the short-term/rapid-response network design following the M7.1 September 2010 Darfield earthquake. Unlike previous seismic network design algorithms, the new algorithm incorporates methods for the realistic representation of 3D velocity structures and attenuation models for both P and S travel times, a surface noise model, and the ability to apply complex weighting functions to the earthquake set. The results demonstrate the utility of this algorithm in even simplistic cases, and show how each new parameter incorporated into the design model affects the optimal network design obtained, identifying the need for accurate input data to provide optimal results.</p>

The Contribution of Submarine Optical Fiber Telecom Cables to the Monitoring of Earthquakes and Tsunamis in the NE Atlantic

Recent developments in optical fiber cable technology allows the use of existing and future submarine telecommunication cables to provide seismic and sea-level information. In this work we study the impact of three different technologies, 1) SMART, Science Monitoring and Reliable Telecommunications; 2) DAS, Distributed Acoustic Sensing, and; 3) LI, Laser Interferometry, for effective earthquake and tsunami monitoring capabilities on the NE Atlantic. The SW Iberia is the source area of the largest destructive earthquake that struck Europe since the year 1000, the November 1, 1755 event. This earthquake generated also a destructive tsunami affecting the whole basin. This tectonically active area is crossed by the CAM (Continent-Azores-Madeira) submarine cable on a ring configuration. Due to the end of cable lifetime the current cables need to be replaced by 2024 and the technical requirements must be defined in mid-2021. The Azores archipelago is the focus of frequent seismic crizes and occasionally destructive earthquakes. A common feature of these seismic events is that they take place offshore, an area that is difficult to monitor from land-based instruments. In this work we evaluate the contribution of SMART cables to the earthquake monitoring and tsunami early warning system in SW Iberia and show how DAS and LI can improve earthquake monitoring on two active domains of the Azores. For tsunami early warning, we show how the offshore sea-level measurements provide clean offshore tsunami records when compared to coastal observations by tide gauges, which greatly improves the efficiency of the system. For earthquake monitoring, the data processing operational routine is examined using Monte-Carlo simulations. These take into consideration the errors in phase picking and the uncertainty on the 1D velocity model used for earthquake location. Quality of earthquake location is examined using the difference between the true location and the centroid of the computed epicenters and by the overall ellipse of uncertainty obtained from 100 runs. The added value provided by instrumented submarine telecommunication cables to mitigate earthquake and tsunami risk demonstrated in this work will help authorities and the society in general to take the political decisions required for its full implementation worldwide.

EARTHQUAKE PREDICTION EVALUATION BASED ON VLF DATA USING A NOVEL INTERSECTION-UNION METHOD

Electromagnetic phenomena, especially those in the Very Low Frequency/Low Frequency (VLF/LF) bands are promising for short-term earthquake prediction. Seismo-ionospheric perturbations cause a variety of changes in different receiver-transmitter VLF/LF signal paths. Therefore, independent and simultaneous observations at different points thus in different VLF/LF signal propagation paths are necessary to better predict the earthquake. Most of the previous research on VLF data have been based on one path or limited number of paths which examined perturbations in the time domain and less attention has been paid to estimate the location of the earthquake. In the present research, using wavelet analysis, the temporal variations of seismo-ionospheric perturbations and the approximate time of earthquake are predicted. Clear disturbances are observed two weeks before the Kumamoto earthquake happened in Japan in 2016. The novelty of this study is to present an approach called Intersection-Union method to predict earthquake location. Based on the geometry of a VLF/LF network, the Intersection-Union method was introduced to estimate the earthquake epicenter. This method is based on the overlay of earthquake occurrence probable areas. With simultaneous use of different propagation paths by the Intersection-Union method, an area with a radius of about 300 km was determined as the probable location of the earthquake epicenter. The accuracy of the proposed method is 300 km compared with 1000 km accuracy of other earthquake location prediction scenarios.

ARRU Phase Picker: Attention Recurrent-Residual U-Net for Picking Seismic P- and S-Phase Arrivals

Seismograms are convolution results between seismic sources and the media that seismic waves propagate through, and, therefore, the primary observations for studying seismic source parameters and the Earth interior. The routine earthquake location and travel-time tomography rely on accurate seismic phase picks (e.g., P and S arrivals). As data increase, reliable automated seismic phase-picking methods are needed to analyze data and provide timely earthquake information. However, most traditional autopickers suffer from low signal-to-noise ratio and usually require additional efforts to tune hyperparameters for each case. In this study, we proposed a deep-learning approach that adapted soft attention gates (AGs) and recurrent-residual convolution units (RRCUs) into the backbone U-Net for seismic phase picking. The attention mechanism was implemented to suppress responses from waveforms irrelevant to seismic phases, and the cooperating RRCUs further enhanced temporal connections of seismograms at multiple scales. We used numerous earthquake recordings in Taiwan with diverse focal mechanisms, wide depth, and magnitude distributions, to train and test our model. Setting the picking errors within 0.1 s and predicted probability over 0.5, the AG with recurrent-residual convolution unit (ARRU) phase picker achieved the F1 score of 98.62% for P arrivals and 95.16% for S arrivals, and picking rates were 96.72% for P waves and 90.07% for S waves. The ARRU phase picker also shown a great generalization capability, when handling unseen data. When applied the model trained with Taiwan data to the southern California data, the ARRU phase picker shown no cognitive downgrade. Comparing with manual picks, the arrival times determined by the ARRU phase picker shown a higher consistency, which had been evaluated by a set of repeating earthquakes. The arrival picks with less human error could benefit studies, such as earthquake location and seismic tomography.