Hierarchical Bayesian Modeling of Fluid‐Induced Seismicity. Issue 22 (23rd November 2017)

• Record Type: Journal Article
• Authors: Broccardo, M.; Mignan, A.; Wiemer, S.; Stojadinovic, B.; Giardini, D.
• Is Part Of: Geophysical research letters. Volume 44:Issue 22(2017)
• Page Start: 11, 357
• Page End: 11, 367
• Publication Date: 2017-11-23
• Subjects: induced‐seismicity -- Bayesian analysis -- epistemic -- aleatory -- forecast -- Poisson process; Geophysics -- Periodicals; Planets -- Periodicals; Lunar geology -- Periodicals; 550
• DOI: 10.1002/2017GL075251
• Languages: English
• ISSNs: 0094-8276

Abstract

Abstract: In this study, we present a Bayesian hierarchical framework to model fluid‐induced seismicity. The framework is based on a nonhomogeneous Poisson process 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. Plain Language Summary: In this study, the authors propose a general and novel probabilistic framework for analyzing and forecasting uncertainties related to fluid‐induced seismicity. Their findings show that the proposed framework enables a novel and more in‐depth understanding of the uncertainties governing fluid‐induced seismicity. Moreover, they show that a new short‐time forecast model formulated using the proposed framework supports an accurate prediction of the number and magnitude of fluid‐induced events. Key Points: Bayesian framework developed for classifying, analyzing, and forecasting uncertainties related to fluid‐induced seismicity Robust classification and treatment of epistemic and aleatory uncertainties in a nonhomogeneous Poisson framework Short‐term forecast model accurately predicts the number and maximum magnitude of events