  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.
