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

Abstract

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%.

A Novel Improved Whale Optimization Algorithm for Global Optimization and Engineering Applications

“…The core idea is to simultaneously consider the opposites of the current solution during the search process with the aim of finding the globally optimal solution faster. Li et al [14] used OBL to initialize the population. Shekhawat et al [27] used OBL to optimize the Crow Search Algorithm.…”

“…The effectiveness of an optimization algorithm is often closely related to the distribution of the initial population. Li et al [14] introduced opposition-based learning to improve the initial distribution of the population. Elmogy et al [15] used two types of discrete chaotic mappings to select the optimal initial population.…”

An Underwater Passive Electric Field Positioning Method Based on Scalar Potential

“…Regarding the selection of location field models, the majority of studies opt for infinite sea areas (non-stratified fields) or semi-infinite sea areas (two-layer stratified fields) as their positioning models [6,7]. The shallow sea (three-layer stratified field) model is infrequently chosen, primarily due to the significant increase in complexity associated with inversion algorithms as the number of media interfaces rises [8][9][10]. Nevertheless, if the application scenario does not align with the assumptions of infinite or semi-infinite fields, the mismatch in positioning models can inevitably lead to a reduction in positioning accuracy.…”