Hierarchical Bayesian modeling of fluid-induced seismicity

TL;DR

This paper introduces a hierarchical Bayesian model for fluid-induced seismicity, effectively capturing uncertainties and providing a reliable short-term forecasting tool validated on real case studies.

Contribution

It develops a novel Bayesian hierarchical framework based on NHPP for modeling and forecasting fluid-induced seismicity, incorporating physical parameters and uncertainties.

Findings

Model successfully applied to six case studies.

Provides robust probabilistic seismic forecasts.

Captures both epistemic and aleatory uncertainties.

Abstract

In this study, we present a Bayesian hierarchical framework to model fluid-induced seismicity. The framework is based on a non-homogeneous Poisson process (NHPP) with a fluid-induced seismicity rate proportional to the rate of injected fluid. The fluid-induced seismicity rate model depends upon a set of physically meaningful parameters, and has been validated for six fluid-induced case studies. In line with the vision of hierarchical Bayesian modeling, the rate parameters are considered as random variables. We develop both the Bayesian inference and updating rules, which are used to develop a probabilistic forecasting model. We tested the Basel 2006 fluid-induced seismic case study to prove that the hierarchical Bayesian model offers a suitable framework to coherently encode both epistemic uncertainty and aleatory variability. Moreover, it provides a robust and consistent short-term seismic forecasting model suitable for online risk quantification and mitigation.