Spatial pattern of the seismicity induced by geothermal operations at the Geysers (California) inferred by unsupervised machine learning
| Autor: | Palo, M., Ogliari, E., Sakwa, M. |
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| Rok vydání: | 2023 |
| Zdroj: | XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) |
| DOI: | 10.57757/iugg23-2635 |
| Popis: | We analyzed the earthquake density at the Geysers geothermal area (California) as 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 labeled as A, B, C, moving from the inner- to the outermost domain, characterized by peculiar time-history of the earthquake rates and different Coulomb stress drops. The earthquake density decreases moving from domain A to C, and different b-values of the Gutenberg-Richter distribution appear for the domains A-B and domain C. We decomposed the mean earthquake rates into their independent components (ICs), which we model as the seismic response to some unknown triggering factors. IC1-2 appear to be straightforwardly related to the wastewater injection, while IC3 contains a secondary contribute from an unknown source. By simple modeling the earthquake source, we estimated that IC3 triggers about 1% of earthquakes of domain A and accounts for a yearly cumulative fault slip of about 7 mm. We broadly localized IC1 in the northern part of the geothermal area where most wells are located; IC2 is instead located about 4 km NW of IC1 source area. We model the earthquake pattern as principally due to stress transfer from the source of IC1 via a poroelastic mechanism. The 28th IUGG General Assembly (IUGG2023) (Berlin 2023) |
| Databáze: | OpenAIRE |
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