The use of Slingram EM38 data for topsoil and subsoil geoelectrical characterization with a Bayesian inversion

Abstract We use the Bayesian method to invert a simple two-layer pedological horizon (1-D with a topsoil and a subsoil) of a surveyed site to be assessed. We show how the Bayesian method is well suited to the determination of topsoil/subsoil features, and can be used in particular as a tool for the analysis of parameters to be retrieved in terms of information content. Our approach is devoted mainly to the assessment of topsoil thickness, and of topsoil and subsoil conductivities, which are provided in terms of probability density functions. We first summarize the methodology implemented with the Geonics EM38-MK2 conductivity meter, and discuss the adaptation of field procedures and post-processing methods to mitigate the effects of drift and bias. We briefly review some non-Bayesian approaches, and then develop the Bayesian approach for the context of our geophysical survey, highlighting its merits. Positivity constraints (on thickness and conductivity) are included in the form of log parameters. A priori knowledge, based on an objective choice made by the geophysicist, is naturally included in the Bayesian scheme. We discuss the equivalence problem associated with the application of the Slingram method to soil structure analysis. The survey of a luvisol at the Kwazulu-Natal (South Africa) site of Potshini is used to illustrate an ecological application of the Slingram and Bayesian methods, used to define the geo-electrical structure of the near-surface soil. These algorithms have demonstrated their usefulness in mapping the clay content of the Bt horizon associated with the control of encroaching trees.