LAUSR

This paper presents the application of a novel trans-dimensional sampling approach to a time domain airborne electromagnetic (AEM) inverse problem to solve for plausible conductivities of the subsurface. Geophysical inverse field problems, such as time domain AEM, are well known to have a large degree of non-uniqueness. Common least-squares optimisation approaches fail to take this into account and provide a single solution with linearised estimates of uncertainty that can result in overly optimistic appraisal of the conductivity of the subsurface. In this new non-linear approach, the spatial complexity of a 2D profile is controlled directly by the data. By examining an ensemble of proposed conductivity profiles it accommodates non-uniqueness and provides more robust estimates of uncertainties. We apply a novel trans-dimensional Bayesian approach using a wavelet parameterisation to airborne electromagnetic (AEM) inversions using data from the Broken Hill region. This approach allows exploration of a range of plausible subsurface conductivity models and provides more robust uncertainty estimates while accounting for potential non-uniqueness.