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3D Aeromagnetic Inversion Using Unsupervised Deep Learning: Imaging Deep Magnetic Structures in the Panxi Region, SW China.

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Title: 3D Aeromagnetic Inversion Using Unsupervised Deep Learning: Imaging Deep Magnetic Structures in the Panxi Region, SW China.
Authors: Zhang, Yu1,2 (AUTHOR), Jian, Chu1,2 (AUTHOR) jianchu_scdzj4@163.com, Cheng, Zhipeng1,2 (AUTHOR), Li, Jun2 (AUTHOR), Xu, Zhengwei2 (AUTHOR), Sui, Chao1 (AUTHOR)
Source: Remote Sensing. May2026, Vol. 18 Issue 9, p1383. 26p.
Subjects: Inversion (Geophysics), Deep learning, Igneous provinces, Magnetite, Rifts (Geology), Magnetic structure, Physiographic provinces, Igneous intrusions
Geographic Terms: Panzhihua (China), China
Abstract: Highlights: What are the main findings? An unsupervised deep learning framework is developed for large-scale 3D aeromagnetic inversion, enabling high-resolution imaging of subsurface magnetization without labeled training data. The inversion reveals two dominant deep magnetic systems in the Panxi region, controlled by N–S and NNE-trending fault structures, corresponding to the Anninghe and Panzhihua rift systems. What are the implications of the main findings? The results demonstrate that deep fault-controlled rift systems play a fundamental role in guiding magma emplacement and controlling the spatial distribution of mafic–ultramafic intrusions. The identified high-magnetization zones provide reliable geophysical indicators for deep exploration targeting of Panzhihua-type V–Ti magnetite deposits. Panzhihua-type V–Ti magnetite deposits in the Panxi region are hosted in mafic–ultramafic intrusions, and their exploration potential depends strongly on the deep distribution of ore-bearing intrusions. High-resolution 3D magnetic inversion is an effective tool to image the geometry of these intrusions. Using 1:50,000 aeromagnetic data, we applied an unsupervised deep learning inversion to obtain the 3D magnetic susceptibility structure of related intrusions. The results show that magnetic anomalies are mainly NS and NEE trending, with minor NNW-trending features. NS-trending sources occur in the Baima–Miyi–Hongge zone between the Xigeda–Yuanmou and Anninghe faults, while NEE-trending anomalies lie west of the Xigeda–Yuanmou fault and east of the Chenghai fault. Integrated geological analysis reveals two Late Variscan rift systems: the Anninghe rift and the Panzhihua rift. Deep fault-controlled magma ascent and emplacement, forming the Emeishan large igneous province, are associated with strongly magnetic intrusions. Mantle plume-derived magmas, differentiated in shallow and deep magma chambers, generate well-differentiated layered complexes at depths < 10 km with magnetic intensities of 5–10 A/m. Shear structures within paleorifts provide favorable emplacement conditions and controlled ore localization. We propose a three-in-one ore-controlling mechanism involving rift systems, intrusive rocks, and shear structures for Panzhihua-type V–Ti magnetite mineralization. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: 3D Aeromagnetic Inversion Using Unsupervised Deep Learning: Imaging Deep Magnetic Structures in the Panxi Region, SW China.
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  Data: &lt;searchLink fieldCode=&quot;JN&quot; term=&quot;%22Remote+Sensing%22&quot;&gt;Remote Sensing&lt;/searchLink&gt;. May2026, Vol. 18 Issue 9, p1383. 26p.
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  Label: Abstract
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  Data: Highlights: What are the main findings? An unsupervised deep learning framework is developed for large-scale 3D aeromagnetic inversion, enabling high-resolution imaging of subsurface magnetization without labeled training data. The inversion reveals two dominant deep magnetic systems in the Panxi region, controlled by N–S and NNE-trending fault structures, corresponding to the Anninghe and Panzhihua rift systems. What are the implications of the main findings? The results demonstrate that deep fault-controlled rift systems play a fundamental role in guiding magma emplacement and controlling the spatial distribution of mafic–ultramafic intrusions. The identified high-magnetization zones provide reliable geophysical indicators for deep exploration targeting of Panzhihua-type V–Ti magnetite deposits. Panzhihua-type V–Ti magnetite deposits in the Panxi region are hosted in mafic–ultramafic intrusions, and their exploration potential depends strongly on the deep distribution of ore-bearing intrusions. High-resolution 3D magnetic inversion is an effective tool to image the geometry of these intrusions. Using 1:50,000 aeromagnetic data, we applied an unsupervised deep learning inversion to obtain the 3D magnetic susceptibility structure of related intrusions. The results show that magnetic anomalies are mainly NS and NEE trending, with minor NNW-trending features. NS-trending sources occur in the Baima–Miyi–Hongge zone between the Xigeda–Yuanmou and Anninghe faults, while NEE-trending anomalies lie west of the Xigeda–Yuanmou fault and east of the Chenghai fault. Integrated geological analysis reveals two Late Variscan rift systems: the Anninghe rift and the Panzhihua rift. Deep fault-controlled magma ascent and emplacement, forming the Emeishan large igneous province, are associated with strongly magnetic intrusions. Mantle plume-derived magmas, differentiated in shallow and deep magma chambers, generate well-differentiated layered complexes at depths &lt; 10 km with magnetic intensities of 5–10 A/m. Shear structures within paleorifts provide favorable emplacement conditions and controlled ore localization. We propose a three-in-one ore-controlling mechanism involving rift systems, intrusive rocks, and shear structures for Panzhihua-type V–Ti magnetite mineralization. [ABSTRACT FROM AUTHOR]
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  Data: &lt;i&gt;Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder&#39;s express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.&lt;/i&gt; (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.3390/rs18091383
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 26
        StartPage: 1383
    Subjects:
      – SubjectFull: Inversion (Geophysics)
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Igneous provinces
        Type: general
      – SubjectFull: Magnetite
        Type: general
      – SubjectFull: Rifts (Geology)
        Type: general
      – SubjectFull: Magnetic structure
        Type: general
      – SubjectFull: Physiographic provinces
        Type: general
      – SubjectFull: Igneous intrusions
        Type: general
      – SubjectFull: Panzhihua (China)
        Type: general
      – SubjectFull: China
        Type: general
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      – TitleFull: 3D Aeromagnetic Inversion Using Unsupervised Deep Learning: Imaging Deep Magnetic Structures in the Panxi Region, SW China.
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            NameFull: Zhang, Yu
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            NameFull: Jian, Chu
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              M: 05
              Text: May2026
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              Y: 2026
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