Inversion of Self-Potential Data from Regular Geometric Objects Using Bat Algorithms

This paper presents an in-depth analysis of the inversion of self-potential (SP) data using the Bat Optimization Algorithm (BA), with a focus on the sphere and cylinder models as representative cases. To establish a performance benchmark and validate the proposed approach, Particle Swarm Optimization (PSO) is employed as a comparative algorithm throughout this study. By leveraging nature-inspired optimization techniques, this study advances geophysical exploration by enhancing the accuracy of subsurface anomaly detection. The primary objective is to evaluate the Bat Algorithm’s effectiveness in estimating model parameters associated with SP anomalies, emphasizing its precision and efficiency. Comprehensive discussions are provided on parameter estimation, including the minimization of misfit and the tuning of algorithm-specific parameters such as loudness and pulse rate. Through meticulous adjustment of these parameters, the BA consistently produces reliable estimates that closely match real-world geological data. The findings highlight the importance of robust parameter tuning strategies for optimizing the algorithm’s performance, especially in complex geophysical environments. The successful application of the BA to both synthetic and real SP data underscores its potential to accurately characterize geological structures with varying geometries, thus contributing valuable insights to the field of geophysical inversion.