mohamed gobashy

Artificial intelligence and metaheuristic approaches had gained a remarkable position in last‐millennium geophysical inversion. The past two decades have witnessed the development of numerous metaheuristics in various communities that sit at the intersection of several fields including geophysics. Many inverse problems in geophysics are considered as constrained optimization, as the aim of the process is to find the best parameter estimates so as to minimize the differences between the predicted results and the observations while satisfying all known constraints or thresholds. Such optimization problems can thus be solved by efficient traditional optimization techniques (e.g.: Least-squares). However, as the number of degrees of freedom is usually very large, metaheuristic algorithms such as, Whale, Grey Wolf, particle swarm, genetic, Bat, and Cuckoo Search algorithms are particularly suitable for inverse problems of that kind, because metaheuristics are very efficient for solving non-linear global optimization problems. The inversion of spontaneous potential (SP) anomalies in particular, attracted many authors. This chapter provides a complete view of metaheuristics as effective tool for parameter estimation from the SP signal. We show the main design questions and search components for selected families of metaheuristics. Not only the design aspect of metaheuristics but also their implementation including the formulation of the Objective/target function. After covering the synthetic examples with the noise tests, many field examples will be presented to show its effectiveness and suitability for various geologic conditions and a diverse range of application domains.