Spatial Pattern of the Seismicity Induced by Geothermal Operations at the Geysers (California) Inferred by Unsupervised Machine Learning

Abstract

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 innerto the outermost domain, and characterized by peculiar timehistory 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 m 2 /s 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.