[M-IS04] Interdisciplinary studies on pre-earthquake processes

[MIS04-P07] Fast Initial Model Design for Electrical Resistivity Inversion by Using Broad Learning Framework

*Tao Tao¹, Peng Han^1,2, Xiao-Hui Yang³, Qiang Zu¹, Kaiyan Hu¹, Shuangling Mo¹, Shuangshuang Li¹, Qiang Luo⁴, Zhanxiang He^1,2 (1.Southern University of Science and Technology, Shenzhen, China, 2.Guangdong Provincial Key Laboratory of Geophysical High-resolution Imaging Technology, Southern University of Science and Technology, Shenzhen, China, 3.Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu, China, 4.Guangdong Provincial Geophysical Prospecting Team, Guangzhou, China)

Keywords:broad learning, 3-D electrical resistivity inversion, limited memory quasi-Newton, near-surface exploration

The electrical resistivity method is widely used in near-surface mineral exploration. At present, the deterministic algorithm is commonly employed in three-dimensional (3-D) electrical resistivity inversion to obtain subsurface electrical structures. However, the accuracy and efficiency of deterministic inversion rely on the initial model. In practice, obtaining an initial model that approximates the true subsurface electrical structures remains challenging. To address this issue, we introduce a broad learning (BL) network to determine the initial model and utilize the limited memory quasi-Newton (L-BFGS) algorithm to conduct the 3-D electrical resistivity inversion task. The powerful mapping capability of the BL network enables one to find the model that elucidates the actual observed data. The single-layer BL network makes it efficient and easy to realize, leading to much faster network training compared to that using the deep learning network. Both the synthetic and field experiments suggest that the BL framework could effectively obtain the initial model based on observed data. Furthermore, in comparison to using a homogeneous medium as the initial model, the L-BFGS inversion with the BL framework-designed initial model improves the inversion accuracy of subsurface electrical structures and expedites the convergence speed of the iteration. This study provides an effective approach for fast initial model design in a da-ta-driven manner when the prior information is unavailable. The proposed method can be useful in high-precision imaging of near-surface mineral electrical structures.