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 labeled as A, B, and 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 domains 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 m2/s is estimated; at larger distances, a poroelastic stress transfer dominates. Our approach can identify spatiotemporal 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.