An efficient Bayesian Framework for Resolving Spatio-Temporal Variations in Background Seismicity through the ETAS Model

The codes implement a Bayesian framework for estimating spatially and temporally varying background seismicity rates within the Epidemic-Type Aftershock Sequence (ETAS) model. The implementation combines a Log-Gaussian Cox Process (LGCP) with the stochastic partial differential equation (SPDE) approach. It includes model estimation, synthetic catalog simulations, and visualization of the inferred background seismicity rate.