A Stochastic Model for Induced Seismicity at the Geothermal Systems: A Case of the Geysers

TL;DR

This paper develops a stochastic model incorporating linear response theory to predict induced seismicity rates due to fluid injection, demonstrated through analysis of the Geysers geothermal field, enhancing earthquake hazard assessment.

Contribution

It introduces a novel stochastic seismicity model based on linear response theory and Bayesian inference, specifically applied to geothermal induced earthquakes.

Findings

Model accurately describes seismicity rate changes due to fluid injection.

Bayesian framework estimates probabilities of large seismic events.

Application to Geysers data validates the model's effectiveness.

Abstract

Induced seismicity has emerged as a source of a significant earthquake hazard associated with recent development of unconventional energy resources. Therefore, it is imperative to develop stochastic models that can accurately describe the observed seismicity rate and forecast its evolution. In this study, a mechanism suggested by linear response theory is incorporated into a stochastic earthquake model to account for changes in the seismicity rate. It is derived that the induced rate can be modelled as a convolution of the forcing, related to fluid injection operations, and a specific response kernel. The model is incorporated into a Bayesian framework to compute the probabilities for the occurrence of the largest expected events during future time intervals. The applicability of the model is illustrated by analyzing the injection and seismicity data at the Geysers geothermal field in California. The suggested approach provides further insight into the probabilistic assessment of earthquake hazard associated with fluid injection operations. It also can be used for probing the rheological properties of the subsurface by analysing the inherent characteristic time-scales associated with the subsurface response to external forcing.