Variability in the b-value, which describes the frequency distribution of earthquake magnitudes, is usually attributed to variations in differential stress in the setting of natural and laboratory earthquakes. However, differential stress is unlikely to explain b-value variations on the reservoir scale of injection-induced seismicity cases where significant differential stress variability can hardly be expected. We investigate the responsible geomechanical parameters for the b-value reduction observed in injection-induced seismicity at the Basel EGS field in Switzerland, for which a measured in-situ stress model and fault orientations of numerous microseismic events are available. We estimate the shear and normal stresses along faults, differential stress, pore pressure increase at failure, and the Coulomb failure stress, for each event. Event magnitude and shear stress display the most systematic and clear correlation between each other, while other parameters do not show a clear correlation with event magnitude. We further examine the relationship between the b-value and these geomechanical parameters. We discover that the b-value systematically decreases with increasing shear stress. Again, other geomechanical parameters do not show a clear correlation with the b-value. We conclude that b-value variability is explained by variations in shear stress in the injection-induced seismicity setting, where near constant differential stress conditions are expected. Furthermore, we observe that b-value reduction with time also correlates with an increasing number of events along faults having high shear stress, which strongly supports our conclusions. Thus, we discovered a profound physical mechanism behind b-value variation in injection-induced seismicity beyond general understandings of b-value variation.

In geothermal development, microseismic monitoring is important technique to monitor the various phenomena in the reservoirs throughout location, activity, and magnitude of microseismicity. Picking P- and S- wave arrivals accurately from seismic data is inevitable process for subsequent seismic analyses. However, the 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, whose accuracy 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 difficulty. 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 distribution was determined using continuous seismic waves in the Okuaizu geothermal field, Japan. These procedures were performed automatically without manual operations and we propose them as MAGMA: Machine learning Automatic picker for Geothermal Microseismicity Analysis. Subsurface fine structures were then revealed by relocating the hypocenters using a double-difference algorithm. The same procedures for the same data were then conducted using a deep learning model that trained by other field data, and the equivalent structures were successfully reveled. Thus, MAGMA is applicable to new fields even when data is lacking, such as green fields.