Implementing a New Inversion Technique of Geophysical Data Incorporating Global Optimization Method Based on Systematic Search, Permutation, and Sensitivity

One of the most important stages in addressing geophysical problems is the inversion procedure of the observed data, which involves incorporating several interdependent model parameters that describe the geophysical properties of the Earth’s subsurface. In this article, we propose a new modification to the inversion technique by applying global optimization method based on systematic search. Global optimization methods are particularly useful because geophysical inverse problems are often nonlinear making traditional local optimization methods less effective. A systematic search using the permutation method for inverting geophysical data refers to a comprehensive and methodical approach that explores different parameter combinations or configurations. The guided permutation method ensures that all possible configurations of model parameters are tested to find the best match between the geophysical model and the observed data. While this method is highly reliable in finding global solutions, its computational cost can be prohibitive for large-scale problems. This issue can be mitigated by conducting an analysis of geophysical parameter sensitivity, which helps determine which model parameters can be reliably estimated, and which are less influential. Furthermore, this analysis enhances the understanding of model uncertainties, guides the inversion process, reduces computational costs, and improves the reliability of the final geophysical model by employing this new inversion technique with the specific method used that is systematic search, permutation, sensitivity analysis. The computer program codes based on systematic search and guided permutation method demonstrates the robustness and accuracy of geophysical inversions. The inversion results from this program, applied to multimode Love wave dispersion curves, show a significant improvement in the quality of the match between the observed and modelled dispersion curves. This improvement is quantitatively confirmed by a decrease in root mean square (RMS) error from 13.435 m/s to 2.887×10 -5 m/s, representing a significant reduction in misfit. Based on the obtained results, the program implementing this new inversion technique, incorporating systematic search, guided permutation, and sensitivity analysis, indicates the robustness and accuracy of geophysical inversions. Finally, we ensure that this inversion technique can be applied to solve other geophysical data problems.