DEVELOPMENT OF A BAYESIAN INVERSION ALGORITHM FOR 1 DIMENSIONAL MAGNETOTELLURIC DATA

Abstract

A Bayesian methodology has been developed for the inversion of magnetotelluric (MT) data. The algorithm provides optimal solutions and also the associated uncertainty for any sets of apparent resistivities and phase at corresponding frequencies from the MT data. The method is based on the 1D forward modelling MT response for a plane-layered anisotropic earth model. ID.MT was chosen for the main tests because of its computational simplicity, which makes it feasible for more demanding inversion. Bayesian inversion of geophysical data has many advantages over the classic deterministic approach. Not only the solution uncertainty can be quantified, large sets of results, sometimes for different parameterizations, provide additional information which can help address interpretational problems with respect to noise level, changes in parameterization etc. Additionally, a large set of results is an interpretational tool itself. If some external geological information can be included, the interpreter is able to choose the best result from the database of accepted results. Sometimes a non-optimal solution that nevertheless has an acceptable misfit can be in better agreement with the geological model then the global best result. The main disadvantage of the Bayesian approach is its computational intensity and its sometimes slow convergence. The results were strongly dependent on the prior knowledge imposed by the prior distribution. The deliverable is a MATLAB program that was tested for synthetic as well as real data sets.