Semiparametric Bayesian Forecasting of Spatial Earthquake Occurrences
This work addresses uncertainty in earthquake forecasting for seismology, but it is incremental as it builds on existing ETAS models with a Bayesian approach.
The authors tackled the problem of forecasting spatial earthquake occurrences by developing a fully Bayesian formulation of the ETAS model, which incorporates a nonparametric Dirichlet process mixture prior for the mainshock process and uses an auxiliary latent variable routine for efficient inference.
Self-exciting Hawkes processes are used to model events which cluster in time and space, and have been widely studied in seismology under the name of the Epidemic Type Aftershock Sequence (ETAS) model. In the ETAS framework, the occurrence of the mainshock earthquakes in a geographical region is assumed to follow an inhomogeneous spatial point process, and aftershock events are then modelled via a separate triggering kernel. Most previous studies of the ETAS model have relied on point estimates of the model parameters due to the complexity of the likelihood function, and the difficulty in estimating an appropriate mainshock distribution. In order to take estimation uncertainty into account, we instead propose a fully Bayesian formulation of the ETAS model which uses a nonparametric Dirichlet process mixture prior to capture the spatial mainshock process. Direct inference for the resulting model is problematic due to the strong correlation of the parameters for the mainshock and triggering processes, so we instead use an auxiliary latent variable routine to perform efficient inference.