[S-EM15] Electric, magnetic and electromagnetic survey technologies and scientific achievements

[SEM15-06] 3-D Magnetic Inversion Based on Broad Learning: An Application to Danzhukeng Pb-Zn-Ag Deposit in South China

*Qiang Zu¹, Tao Tao¹, Xiao-hui Yang³, Shuangling Mo¹, Shuangshuang li¹, Peng Han^1,2 (1.Southern University of Science and Technology, Shenzhen 518055, China, 2.Guangdong Provincial Key Laboratory of Geophysical High-resolution Imaging Technology, Southern University of Science and Technology, Shenzhen 518055, China, 3.Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu, 610225, China)

Keywords: 3-D magnetic inversion, broad learning, magnetic anomaly, Danzhukeng Pb-Zn-Ag Deposit

Three-dimensional (3-D) magnetic inversion is an essential technique for revealing the distribution of subsurface magnetization structures. Conventional methods are often time-consuming and suffer from inadequate depth resolution due to limited observations and ambiguity. To address these limitations, we propose a novel inversion method under the machine learning framework. First, we design a training sample generation space by extracting the horizontal positions of magnetic sources from the magnetic field data. We then employ coordinate transformation to achieve data augmentation within the designed space. Subsequently, we utilize the Broad Learning network to map magnetic anomaly to 3-D magnetization structures, reducing the magnetic inversion time. The accuracy and efficiency of the proposed method are validated through both synthetic and field data. Synthetic examples indicate that the proposed method achieves superior depth resolution compared to conventional smooth inversion technique and offers improved efficiency over both conventional and deep learning methods. In the field example of Danzhukeng Pb-Zn-Ag deposit in South China, the inversion result is consistent with drilling and controlled-source audio frequency magnetotelluric survey data, providing valuable insights for subsequent exploration. This study provides a new practical tool for processing and interpreting magnetic anomaly data.