  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.
