Bayesian Detectability of Induced Polarisation in Airborne Electromagnetic Data using Reversible Jump Sequential Monte Carlo

TL;DR

This paper introduces a Bayesian Reversible Jump Sequential Monte Carlo method for detecting induced polarisation effects in airborne electromagnetic data, providing a new statistical framework for geophysical model inference.

Contribution

It develops a novel Bayesian transdimensional inference algorithm (RJSMC) for IP detectability, integrating model adaptivity and addressing particle impoverishment issues in geophysical applications.

Findings

Successful application to AEM data demonstrating IP detectability.

Improved inference of model parameters and model odds.

Enhanced robustness of Bayesian model selection in geophysics.

Abstract

Detection of induced polarisation (IP) effects in airborne electromagnetic (AEM) measurements does not yet have an established methodology. This contribution develops a Bayesian approach to the IP-detectability problem using decoupled transdimensional layered models, and applies an approach novel to geophysics whereby transdimensional proposals are used within the embarrassingly parallelisable and robust static Sequential Monte Carlo (SMC) class of algorithms for the simultaneous inference of parameters and models. Henceforth referring to this algorithm as Reversible Jump Sequential Monte Carlo (RJSMC), the statistical methodological contributions to the algorithm account for adaptivity considerations for multiple models and proposal types, especially surrounding particle impoverishment in unlikely models. Methodological contributions to solid Earth geophysics include the decoupled model approach and proposal of a statistic that use posterior model odds for IP detectability. A case study is included investigating detectability of IP effects in AEM data at a broad scale.