Joint inversion: A structural approach

We develop a methodology to invert two different data sets with the assumption that the underlying models have a common structure. Structure is defined in terms of absolute value of curvature of the model and two models are said to have common structure if the changes occur at the same physical locations. The joint inversion is solved by defining an objective function which quantifies the difference in structure between two models, and then minimizing this objective function subject to satisfying the data constraints. The problem is nonlinear and is solved iteratively using Krylov space techniques. Testing the algorithm on synthetic data sets shows that the joint inversion is superior to individual inversions. In an application to field data we show that the data sets are consistent with models that are quite similar.