Machine learning automatic picker for geothermal microseismicity analysis for practical procedure to reveal fine reservoir structures

In geothermal development, microseismic monitoring is an important technique to monitor the various phenomena in the reservoirs throughout the location, activity, and magnitude of microseismicity. Picking P- and Swave arrivals accurately from seismic data is an inevitable process for subsequent seismic analyses. However, manual phase picking is a time- and cost-consuming process and several automatic pickers still requires considerable quality checks and corrections by human analysts. Automatic pickers based on deep learning have recently been developed for natural earthquake analysis, the accuracy of which has been confirmed to be comparable to that of human analysts. These phase pickers were mainly trained using natural earthquakes recorded by regional seismic networks. However, seismic networks and events in geothermal fields have features that differ from those of natural earthquakes. In such fields, seismic events with very low magnitudes occur immediately under the seismic network and are sometimes triggered by fluid activity. Therefore, the direct application of the existing deep learning phase pickers to such seismic networks may have challenges. Here, we focus on developing a deep learning model specialized for local seismic networks in geothermal fields. We used microseismic data from four representative enhanced geothermal and hydrothermal fields and trained the model with deep learning. Based on the developed model, the hypocenter determinations were conducted using continuous seismic waves in the Okuaizu geothermal field, Japan. These procedures were performed automatically without manual operations. Subsurface fine structures were then revealed by relocating the hypocenters using a double-difference algorithm. We also confirmed that our developed model is applicable to new fields, where existing seismic data is not sufficient to perform additional training.