Geophysical joint inversion based on mixed structural and rock-physics coupling constraints

Joint inversion algorithms continue to receive many interests in the application in geophysical interpretation due to reducing the uncertainty of the inverse solutions. Over the last decades, several coupling constraints, structural or rock physics, have been introduced to the joint inversion of multiple geophysical data sets. However, a single coupling constraint may not provide the optimum solution for all geological scenarios. In order to improve the application of the multiple geophysical data sets, when some rock physics information is available, we present a new joint inversion algorithm based on a mixed structural and rock physics coupling constraints. The presented algorithm incorporates the cosine dot product gradient function, a structural coupling constraint, with guided fuzzy c-means clustering strategy, which is a rock physics coupling constraint. The former imposes the structural similarity between different model parameters, while the latter improves the linear or non-linear correlation between model parameters. We use the exact structural resemblance algorithm to numerically solve the mixed coupling constraint joint inversion objective function. The developed algorithm is applied on two-dimensional synthetic and field data examples. Our results show a significant improvement in the resolution of the reconstructed models, in comparison with traditional single coupling algorithms, even when the rock physics information is not complete. This algorithm is more conducive to obtaining clear geological unit boundaries and provides a favorable basis for delineating metallogenic target areas.