Global Joint Inversion Of Tomographic Data: Appraisal Of Model Reconstruction Ambiguity

Geophysical model reconstruction by data inversion is usually ill-posed and suffers ambiguity due to limited number and accuracy of the available observations. Joint inversion of different data sets allows for mutually improved reconstruction of physical parameter models underlying each of the available data sets, but considering the limited number and accuracy of available observations, some ambiguity remains. Here, we use particle swarm optimization to jointly invert synthetic GPR and Pwave crosshole tomographic data sets. Model parameterization is guided by the results of a zonal cooperative inversion based on local search optimization of an initial guess. Global optimization is first done to explore the Pareto front of the joint inverse problem in a very efficient way. Consecutively, the area behind a selected location of the Pareto front is explored to be able to assess the model reconstruction ambiguity inherent to the available data and chosen parameterization.