BayesMT: Bayesian Inference for the Seismic Moment Tensor Using Regional and Teleseismic-P Waveforms with First-motion Data and a Calibrated Prior Distribution of Velocity Models

Abstract:

The largest source of uncertainty in any source inversion is the velocity model used to construct the transfer function employed in the forward model that relates observed ground motion to the seismic moment tensor. We attempt to incorporate this uncertainty into an estimation of the seismic moment tensor using a posterior distribution of velocity models based on different and complementary data sets, including thickness constraints, velocity profiles, gravity data, surface wave group velocities, and regional body wave traveltimes. The posterior distribution of velocity models is then used to construct a prior distribution of Green's functions for use in Bayesian inference of an unknown seismic moment tensor using regional and teleseismic-P waveforms with first-motion data. The use of multiple data sets is important for gaining resolution to different components of the moment tensor. The combined likelihood is estimated using data-specific error models and the posterior of the seismic moment tensor is estimated and interpreted in terms of most-probable source-type. Prepared by LLNL under Contract DE-AC52-07NA27344.