Separation of the Total Magnetic Anomalies into Induced and Remanent Magnetization Based on Deep Learning

Li, Weichen; Wang, Jun; Meng, XiaoHong; Xi, Biao

doi:10.1007/s00024-023-03391-y

Pure Appl. Geophys.

2023

DOI: 10.1007/s00024-023-03391-y

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Separation of the Total Magnetic Anomalies into Induced and Remanent Magnetization Based on Deep Learning

Weichen Li,

Jun Wang,

XiaoHong Meng

et al.

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Cited by 3 publications

References 32 publications

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Extraction of Remanent Magnetization Intensity and Direction Based on ResU-Net

Li,

Wang,

et al. 2025

IEEE Geosci. Remote Sensing Lett.

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Extraction of Remanent Magnetization Intensity and Direction Based on ResU-Net

Li,

Wang,

et al. 2025

IEEE Geosci. Remote Sensing Lett.

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A Stable Method for Estimating the Derivatives of Potential Field Data Based on Deep Learning

Liu,

Wang,

et al. 2025

IEEE Geosci. Remote Sensing Lett.

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An Analysis and Interpretation of Magnetic Data of the Qing-Chengzi Deposit in Eastern Liaoning (China) Area: Guide for Structural Identification and Mineral Exploration

Li,

Wang,

Meng

et al. 2024

Minerals

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Qing-Chengzi (QCZ) is an important silver-gold mining area in the eastern part of the Northeast China Craton. The shallow minerals in this area are almost completely depleted, leading to a demand for exploration to find deeper, concealed deposits. However, due to the rugged terrain, few high-precision ground surveys have been executed in this area, resulting in an insufficient understanding of the unexposed ores. To address this issue, this study implemented a high-precision ground magnetic survey to identify faults and potential rocks in this area. To achieve these goals, remanence was analyzed to reduce its adverse effect on processing. Then, lineament enhancement with directional derivatives was conducted on the pre-processed magnetic anomalies to highlight structural features. Based on the results, eight major and twenty-one minor faults were identified, among which three major faults correspond well to the known faults. Most of the major faults run N–S, and the others run NW/NE. Furthermore, 3D inversion was conducted to locate potential rocks. Our inversion results indicate that there are six hidden rocks in the underground, extending from a depth of a few hundred meters to no more than three km. Two of the rocks correspond well to the already mined areas. This study provides support for subsequent exploration in the QCZ area.

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Separation of the Total Magnetic Anomalies into Induced and Remanent Magnetization Based on Deep Learning

Cited by 3 publications

References 32 publications

Extraction of Remanent Magnetization Intensity and Direction Based on ResU-Net

Extraction of Remanent Magnetization Intensity and Direction Based on ResU-Net

A Stable Method for Estimating the Derivatives of Potential Field Data Based on Deep Learning

An Analysis and Interpretation of Magnetic Data of the Qing-Chengzi Deposit in Eastern Liaoning (China) Area: Guide for Structural Identification and Mineral Exploration

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