Fast Initial Model Design for Electrical Resistivity Inversion by Using Broad Learning Framework

Tao, Tao1; Han, Peng1,2; Yang, Xiao-Hui3; Zu, Qiang1; Hu, Kaiyan1; Mo, Shuangling1; Li, Shuangshuang1; Luo, Qiang4; He, Zhanxiang1,2

2024-02-01

10.3390/min14020184

MINERALS   影响因子和分区

2075-163X

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 data-driven manner when the prior information is unavailable. The proposed method can be useful in high-precision imaging of near-surface mineral electrical structures.

broad learning 3-D electrical resistivity inversion limited memory quasi-Newton near-surface mineral exploration

SCI

英语

第一 ; 通讯

Geochemistry & Geophysics ; Mineralogy ; Mining & Mineral Processing

Geochemistry & Geophysics ; Mineralogy ; Mining & Mineral Processing

WOS:001172500900001

MDPI

Web of Science

1.Southern Univ Sci & Technol, Dept Earth & Space Sci, Shenzhen 518055, Peoples R China
2.Southern Univ Sci & Technol, Guangdong Prov Key Lab Geophys High Resolut Imagin, Shenzhen 518055, Peoples R China
3.Chengdu Univ Informat Technol, Sch Atmospher Sci, Plateau Atmosphere & Environm Key Lab Sichuan Prov, Chengdu 610225, Peoples R China
4.Guangdong Prov Geophys Prospecting Team, Guangzhou 510080, Peoples R China

Tao, Tao,Han, Peng,Yang, Xiao-Hui,et al. Fast Initial Model Design for Electrical Resistivity Inversion by Using Broad Learning Framework[J]. MINERALS,2024,14(2).

Tao, Tao.,Han, Peng.,Yang, Xiao-Hui.,Zu, Qiang.,Hu, Kaiyan.,...&He, Zhanxiang.(2024).Fast Initial Model Design for Electrical Resistivity Inversion by Using Broad Learning Framework.MINERALS,14(2).

Tao, Tao,et al."Fast Initial Model Design for Electrical Resistivity Inversion by Using Broad Learning Framework".MINERALS 14.2(2024).