Detecting the Preparatory Phase of Induced Earthquakes at The Geysers (California) Using K‐Means Clustering

Abstract

The generation of strong earthquakes is a long‐debated problem in seismology, and its importance is increased by the possible implications for earthquake forecasting. It is hypothesized that the earthquake generation processes are anticipated by several phenomena occurring within a nucleation region. These phenomena, also defined as preparatory processes, load stress on the fault leading it to reach a critical state. In this paper, we investigate the seismicity preceding 19 moderate (Mw ≥ 3.5) earthquakes at The Geysers, Northern California, aiming to verify the existence of a preparatory phase before their occurrence. We apply an unsupervised K‐means clustering technique to analyze time series of physics‐related features extracted from catalog information and estimated for events occurred before the mainshocks. Specifically, we study the temporal evolution of the b‐value from the Gutenberg‐Richter (b), the magnitude of completeness (Mc), the fractal dimension (Dc), the inter‐event time (dt), and the moment rate (). Our analysis shows that out of 19 moderate magnitude events considered, a common preparatory phase for 11 events is clearly identified, plus other five events for which we can guess a preparatory phase but with different characteristics from the previous ones. The latter result confirms that even within the same tectonic context different possible activation behaviors may exist. The duration of the preparatory process ranges between about 16 hr and 4 days. We observe that also for the retrieved preparatory process a decrease in b, Mc, and Dc, and an increase of . Finally, we show a clear correlation between events with a preparation phase and the location of injection's wells, suggesting an important role of fluids in the preparatory process.

Deep learning forecasting of large induced earthquakes via precursory signals

“…AI techniques can learn significant patterns from the data to generate models to support and sustain human expertise. In particular, different approaches from Machine Learning (ML) and Deep Learning (DL) are successfully applied in the study of earthquakes 15 – 18 and their detection 19 – 21 , with also some application in the field of induced seismicity 22 , and particular aimed at trying to anticipate the location of the areas where earthquakes are expected to occur 23 . According to 24 earthquake catalogues collected by using AI have reached an unprecedented quality and detail that can help seismologists formulate and test new hypotheses about precursors of large earthquakes.…”