In the joint inversion of geophysical data measured above 2D or 3D geological structures non-uniqueness and ambiguity problems often occur. An other problem in integrating various data sets in a single inversion algorithm (simultaneous- or joint inversion) is the use of appropriate weights regulating the contribution of the given data set to the solution. In order to reduce non-uniqueness and ambiguity problems in this paper we use series expansion in the discretization of the laterally varying model parameters resulting in a much lower number of unknowns. In the joint inversion of two data sets containing sufficiently different level of noise we apply optimized weights for balancing between the data sets. It is demonstrated in a numerical experiment that the application of optimized weights together with the use of series expansion in discretization results in stable and reliable joint inversion.


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