Abstract: In 2017 the National Power Company in Iceland, Landsvirkjun, started the operation of a geothermal power plant in Theistareykir (Northeastern Iceland). The plant’s operation requires extraction, circulation, and injection of the geothermal fluids to produce energy. These processes depend on the existing fracture network of the reservoir. Therefore, geothermal energy exploitation requires knowledge of underground structures to identify potential fluid flow pathways. These are, in many cases, evidenced by the local seismicity. In this context, the GFZ German Research Center for Geosciences and Landsvirkjun chose this site for deploying a dense network of fifteen seismic broadband stations to monitor and characterize the field’s seismicity. The data coming from this very dense network allows us to implement and test an optimized processing scheme to perform a detailed analysis of the local seismicity. This study’s primary goal is to implement an efficient and reliable scheme to characterize the local seismicity of the Theistareykir geothermal field using the collected high-resolution seismic data from the dense network. I used several traditional earthquake seismology methods to detect, analyze, classify, and localize, repeating microseismic events. I first used a recursive STA/LTA algorithm to detect the local seismicity between January 1, 2018, until June 30, 2018. Using the detections, I manually reviewed and picked P- and Sphase arrival times. After an initial non-linear localization, I performed a correlation clustering analysis and identified two events with a high degree of waveform similarity. I corrected the picked phase arrivals using the cross-correlation coefficients of the events to a master trace. These events were relocated to improve their relative locations. Events of both clusters will be used in future studies for template-matching to detect and pick additional events within this period. The methodology applied here is meant as a guide to process upcoming seismic data of the geothermal field and efficiently perform a full seismological characterization using more massive datasets.

Abstract: In 2017 the National Power Company in Iceland, Landsvirkjun, started the operation of a geothermal power plant in Theistareykir (Northeastern Iceland). The plant’s operation requires extraction, circulation, and injection of the geothermal fluids to produce energy. These processes depend on the existing fracture network of the reservoir. Therefore, geothermal energy exploitation requires knowledge of underground structures to identify potential fluid flow pathways. These are, in many cases, evidenced by the local seismicity. In this context, the GFZ German Research Center for Geosciences and Landsvirkjun chose this site for deploying a dense network of fifteen seismic broadband stations to monitor and characterize the field’s seismicity. The data coming from this very dense network allows us to implement and test an optimized processing scheme to perform a detailed analysis of the local seismicity. This study’s primary goal is to implement an efficient and reliable scheme to characterize the local seismicity of the Theistareykir geothermal field using the collected high-resolution seismic data from the dense network. I used several traditional earthquake seismology methods to detect, analyze, classify, and localize, repeating microseismic events. I first used a recursive STA/LTA algorithm to detect the local seismicity between January 1, 2018, until June 30, 2018. Using the detections, I manually reviewed and picked P- and Sphase arrival times. After an initial non-linear localization, I performed a correlation clustering analysis and identified two events with a high degree of waveform similarity. I corrected the picked phase arrivals using the cross-correlation coefficients of the events to a master trace. These events were relocated to improve their relative locations. Events of both clusters will be used in future studies for template-matching to detect and pick additional events within this period. The methodology applied here is meant as a guide to process upcoming seismic data of the geothermal field and efficiently perform a full seismological characterization using more massive datasets.

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Abstract: In 2017 the National Power Company in Iceland, Landsvirkjun, started the operation of a geothermal power plant in Theistareykir (Northeastern Iceland). The plant’s operation requires extraction, circulation, and injection of the geothermal fluids to produce energy. These processes depend on the existing fracture network of the reservoir. Therefore, geothermal energy exploitation requires knowledge of underground structures to identify potential fluid flow pathways. These are, in many cases, evidenced by the local seismicity. In this context, the GFZ German Research Center for Geosciences and Landsvirkjun chose this site for deploying a dense network of fifteen seismic broadband stations to monitor and characterize the field’s seismicity. The data coming from this very dense network allows us to implement and test an optimized processing scheme to perform a detailed analysis of the local seismicity. This study’s primary goal is to implement an efficient and reliable scheme to characterize the local seismicity of the Theistareykir geothermal field using the collected high-resolution seismic data from the dense network. I used several traditional earthquake seismology methods to detect, analyze, classify, and localize, repeating microseismic events. I first used a recursive STA/LTA algorithm to detect the local seismicity between January 1, 2018, until June 30, 2018. Using the detections, I manually reviewed and picked P- and Sphase arrival times. After an initial non-linear localization, I performed a correlation clustering analysis and identified two events with a high degree of waveform similarity. I corrected the picked phase arrivals using the cross-correlation coefficients of the events to a master trace. These events were relocated to improve their relative locations. Events of both clusters will be used in future studies for template-matching to detect and pick additional events within this period. The methodology applied here is meant as a guide to process upcoming seismic data of the geothermal field and efficiently perform a full seismological characterization using more massive datasets.