Inversion of self-potential anomalies using grey wolf optimization

This study presents the application of the Grey Wolf Optimization (GWO) algorithm for the quantitative inversion of self-potential (SP) anomalies originating from simple geometrical sources. The method estimates seven physical and geometrical parameters that define an SP anomaly: electrical dipole moment, polarization angle, depth, shape factor, horizontal position, base slope, and base level. To evaluate the performance and stability of the algorithm, synthetic data tests were conducted under both noise-free and Gaussian noise–contaminated conditions. In both cases, the GWO inversion successfully recovered the true model parameters with rapid convergence and high precision, demonstrating strong robustness to noise and insensitivity to initial conditions. The method was subsequently applied to two classical field anomalies, Süleymanköy , Weiss, and Sarıyer which have been widely used as benchmarks in SP interpretation studies. The inversion results showed good agreement with previous studies employing other metaheuristic algorithms, confirming the reliability and efficiency of GWO in real-world applications with competitive convergence performance. Overall, the findings highlight the potential of the GWO algorithm as a powerful and computationally efficient tool for SP data interpretation. Its capability to achieve accurate parameter estimation with minimal parameter tuning makes it a promising approach for broader applications in non-linear and multi-parameter geophysical inversion problems.