Dynamic Error Bat Algorithm: Theory and Application to Magnetotelluric Inversion

Metallic minerals and some nonmetallic deposits (such as gas hydrate and natural gas) exhibit significant resistivity contrast with their surrounding rocks. Therefore, magnetotelluric (MT) sounding, which is highly sensitive to low-resistivity anomalies, offers a unique advantage in identifying these mineral resources. For metallogenic systems in sedimentary environments with approximately layered structures, we propose the Dynamic Error Bat Algorithm (DEBA), which integrates the cooling strategy, the dynamized fit error function, and the Bat Algorithm. DEBA enhances the breadth of global exploration in the early iteration stages while focusing on the depth of local exploitation in the later stages, yielding a more effective fitting outcome and better identification of electrical interfaces. Validity and noise immunity tests on typical synthetic models prove the robustness of DEBA. For broadband MT stations from the central Songliao Basin, we observed that the model derived from three-dimensional inversion did not provide an ideal layering effect for the shallow structure. Notably, the apparent resistivity and phase curves of these MT stations are similar, suggesting that the shallow structure in the study area has approximately one-dimensional (1-D) features, a conclusion that was further supported by phase tensor analysis. To gain a clearer understanding of the shallow structure, we applied DEBA to perform an averaged 1-D inversion. The subsequent results reveal a low-resistivity layer, which may be attributed to metallic sulfides or saline fluids.