Spatial pattern of the seismicity induced by geothermal operations at the Geysers (California) inferred by unsupervised machine learning

We analyzed the earthquake density of the Geysers geothermal field (California) as a function of time and space over a decade. We grouped parts of the volume of the geothermal area sharing similar earthquake rates over time; in this way, we found three concentric spatial domains centered on the principal exploitation area and labelled as A, B, C, moving from the inner-to the outermost domain, and characterized by peculiar time-history of the earthquake rates and different stress conditions. The earthquake density decreases moving from domain A to C, and different slopes of the earthquake frequency-magnitude distribution appear for the domains A-B and domain C. Stress field propagates via a diffusive mechanism up to about 3.5 km from the center of the geothermal area outwards, and a mean hydraulic diffusivity of about 0.05 2/ is estimated; at larger distances a poroelastic stress transfer dominates. Our approach can identify spatio-temporal patterns of physical mechanisms driving induced seismicity and can in principle be extended to other settings of man-induced earthquakes. Moreover, it potentially allows a differentiated assessment of the seismic risk within each domain, as well as the identification of domains with no or minimal induced seismicity