Exploring Fault Plane Geometry through Metaheuristic Bat Algorithm (MBA) Analysis of Potential Field Data: Environmental and Engineering Applications

Abstract By integrating inversion techniques with modeling data of the Earth’s passive potential field, encompassing gravity and magnetic fields, we can enhance our understanding of subsurface structural features, particularly faults, thereby contributing to advancements in earth science and environmental studies. Metaheuristic algorithms have gained prominence as global optimization tools, with increasing utilization for optimizing complex systems. This study proposes the utilization of the Metaheuristic Bat Algorithm (MBA), inspired by the echolocation capabilities of bats, to efficiently search for optimal solutions. The MBA method aims to minimize a predefined objective function, leading to the identification of fault-path parameters once the global optimum solution is attained. This approach offers a systematic means of evaluating fault characteristics without requiring prior domain knowledge. Application of the MBA methodology to potential field data facilitates the estimation of fault dimensions, including depth, origin, and dipping angle. Through rigorous testing on diverse simulated datasets with varying noise levels, the MBA approach demonstrates high precision and consistency in fault characterization. Moreover, field applications conducted in the USA, Egypt, Australia, and India validate the efficacy of the MBA scheme in earth science and engineering investigations. The inversion results obtained using the MBA approach align closely with drilling data, geologic observations, and existing literature, underscoring its reliability and utility in subsurface analysis.