Sequential Bayesian techniques applied to non‐volcanic tremor

Abstract

This paper uses sequential Bayesian techniques such as particle filters and smoothers to track in time both the non‐volcanic tremor (NVT) source location on the plate interface and the angle of arrival via horizontal phase slowness. Sequential Bayesian techniques enable tracking of evolving geophysical parameters via sequential tremor observations. These techniques provide a formulation where the geophysical parameters that characterize dynamic, non‐stationary processes are continuously estimated as new data become available. In addition to the optimal solution, particle filters and smoothers can calculate the underlying probability densities for the desired parameters, providing the uncertainties in the estimates. The tremor tracking has been performed using array beamforming. Here it is demonstrated that the uncertainties both in the NVT source location estimates and phase slowness estimates are reduced using a particle filter compared to just using a beamformer based inversion. Particle smoothers further reduces the uncertainty, giving the best performance out of the three methods used here.