Enhanced Whale Optimization Algorithm for Improved Transient Electromagnetic Inversion in the Presence of Induced Polarization Effects

The transient electromagnetic (TEM) method is a non-contact technique used to identify underground structures, commonly used in mineral resource exploration. However, the induced polarization (IP) will increase the nonlinearity of TEM inversion, and it is difficult to predict the geoelectric structure from TEM response signals in conventional gradient inversion. We select a heuristic algorithm suitable for nonlinear inversion-a whale optimization algorithm to perform TEM inversion with an IP effect. The inverse framework is optimized by opposition-based learning (OBL) and an adaptive weighted factor (AWF). OBL improves initial population distribution for better global search, while the AWF replaces random operators to balance global and local search, enhancing solution accuracy and ensuring stable convergence. Tests on layered geoelectric models demonstrate that our improved WOA effectively reconstructs geoelectric structures, extracts IP information, and performs robustly in noisy environments. Compared to other nonlinear inversion methods, our proposed approach shows superior convergence and accuracy, effectively extracting IP information from TEM signals, with an error of less than 8%.

Automated summary

The transient electromagnetic (TEM) method is a non-contact technique commonly used in mineral resource exploration to identify underground structures. However, the induced polarization (IP) introduces nonlinearity in TEM inversion, making it difficult to predict the geoelectric structure from TEM response signals. In this study, we propose an improved whale optimization algorithm (WOA) with opposition-based learning (OBL) and adaptive weighted factor (AWF) to address the IP effect in TEM inversion. Our tests on layered geoelectric models demonstrate the effectiveness of our approach in reconstructing geoelectric structures and extracting IP information, with superior convergence and accuracy compared to other nonlinear inversion methods.