Bayesian detectability of induced polarization in airborne electromagnetic data

Detection of induced polarization (IP) effects in airborne electromagnetic measurements does not yet have an established methodology. This work contributes 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 parallelizable and robust sequential Monte Carlo class of algorithms for the simultaneous inference of parameters and models. This algorithm allows for adaptivity considerations for multiple models and proposal types. Methodological contributions to solid Earth geophysics include the decoupled layered model approach and proposal of a statistic that uses posterior model odds for IP detectability. A case study is included investigating the detectability of IP effects in airborne electromagnetic data at a broad scale.

Automated summary

This study presents a Bayesian approach to detect induced polarization effects in airborne electromagnetic measurements. It introduces a novel statistic using decoupled layered models and proposals, and investigates the detectability of IP effects in airborne electromagnetic data at a broad scale through a case study.