Method for adaptive separation of post-seismic deformations induced by different mechanisms of major earthquakes based on a bayesian framework

Abstract

Disclosed is a method for adaptive separation of post-seismic deformations induced by different mechanisms of major earthquakes based on a Bayesian framework, including acquiring pure post-seismic displacement data observed by a Global Navigation Satellite 5 System (GNSS) station network; constructing a comprehensive surface response model incorporating the post-seismic afterslip effect and viscoelastic relaxation effect, and associating the two mechanisms with their respective subsurface source model parameters and temporal attenuation characteristic parameters; defining physically meaningful ranges for the parameters; performing joint inversion of all parameters via the Bayesian 10 framework and Markov Chain Monte Carlo (MCMC) algorithm to obtain their posterior probability density functions and optimal solutions; and finally, using the optimal solutions to achieve adaptive separation of the two effects in surface deformation and resolve the spatial distribution of their respective source models. This invention provides an accurate, reliable and uncertainty-quantifiable analysis tool for in-depth understanding of 15 post-seismic processes of major earthquakes. FIG. 1