Using physics-based priors in a Bayesian algorithm to enhance infrasound source location

Abstract

We show improvements in the precision of the Bayesian infrasound source localization (BISL) method by incorporating semi-empirical model-based prior information. Given a set of backazimuths and delay times at ≥2 arrays, BISL scans a parameter space (that comprises the horizontal coordinates, celerity and origin time) for the most likely solution. A key element of BISL is its flexibility; the method allows the incorporation of prior information to constrain the parameters. Our research focuses on generating model-based propagation catalogues using a comprehensive set of atmospheric scenarios, extracting celerity distributions based on range and azimuth from the catalogues and using these distributions as prior probability density functions to enhance the location solution from BISL. To illustrate the improvements in source location precision, we compare the BISL results computed using uniform celerity distribution priors with those using enhanced priors; as applied to: (1) a set of events recorded across a regional network and (2) a large accidental chemical explosion recorded by six infrasound arrays in Eurasia. Finally, we discuss efforts to improve the numerical implementation of BISL by expanding the parameter space to cover a richer set of parameters that can include station-specific celerity distributions.