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Electromagnetic Inversion for Deep Ore Explorations
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Electromagnetic Inversion for Deep Ore Explorations

A special issue of Minerals (ISSN 2075-163X). This special issue belongs to the section "Mineral Exploration Methods and Applications".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 690

Special Issue Editors

Dr. Ronghua Peng

E-Mail Website
Guest Editor
School of Geophysics and Geomatics, China University of Geosciences (Wuhan), Wuhan 430074, China
Interests: geophysical electromagnetic; forward modeling; inversion methods; applications in mineral and geothermal explorations
Dr. Bo Han

E-Mail Website
Guest Editor
Institute of Geological Survey, China University of Geosciences (Wuhan), Wuhan 430074, China
Interests: forward modeling; inversion algorithms; electromagnetic methods
Dr. Yuanzhi Cheng

E-Mail Website
Guest Editor
Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China
Interests: magnetotellurics; geothermics; geodynamics

Special Issue Information

Dear Colleagues,

The increasing global demand for mineral resources has driven the need for advanced geophysical techniques to explore deep and complex ore deposits. Geophysical electromagnetic (EM) methods have emerged as a powerful tool for subsurface exploration due to their sensitivity to variations in electrical conductivity, which is often associated with mineralization. However, the interpretation of EM data in deep and geologically complex environments remains a significant challenge, requiring robust and innovative inversion techniques to extract meaningful subsurface information.

This Special Issue focuses on the latest advancements in electromagnetic inversion methods tailored for deep ore exploration. Our aim is to bring together cutting-edge research and practical applications that address the challenges of imaging deep and heterogeneous geological structures. Topics of interest include, but are not limited to, novel inversion algorithms, improvements in computational efficiency, the joint inversion of EM data with other geophysical methods, and case studies demonstrating the application of EM inversion in real-world ore exploration scenarios. The submission of contributions exploring the integration of machine learning and artificial intelligence in EM inversion is also encouraged, as these approaches hold great potential for enhancing the resolution and reliability of subsurface models.

Dr. Ronghua Peng
Dr. Bo Han
Dr. Yuanzhi Cheng
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Minerals is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • electromagnetic inversion
  • deep ore exploration
  • mineral prospecting
  • machine learning
  • time-domain and frequency-domain EM methods

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Published Papers (2 papers)

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Research

22 pages, 5215 KiB  
Article
Analysis and Modeling of Elastic and Electrical Response Characteristics of Tight Sandstone in the Kuqa Foreland Basin of the Tarim Basin
by Juanli Cui, Kui Xiang, Xiaolong Tong, Yanling Shi, Zuzhi Hu and Liangjun Yan
Minerals 2025, 15(7), 764; https://doi.org/10.3390/min15070764 - 21 Jul 2025
Viewed by 172
Abstract
This study addresses the limitations of conventional evaluation methods caused by low porosity, strong heterogeneity, and complex pore structures in tight sandstone reservoirs. Through integrated rock physics experiments and multi-physical field modeling, the research systematically investigates the coupled response mechanisms between electrical and [...] Read more.
This study addresses the limitations of conventional evaluation methods caused by low porosity, strong heterogeneity, and complex pore structures in tight sandstone reservoirs. Through integrated rock physics experiments and multi-physical field modeling, the research systematically investigates the coupled response mechanisms between electrical and elastic parameters. The experimental approach includes pore structure characterization, quantitative mineral composition analysis, resistivity and polarizability measurements under various saturation conditions, P- and S-wave velocity testing, and scanning electron microscopy (SEM) imaging. The key findings show that increasing porosity leads to significant reductions in resistivity and elastic wave velocities, while also increasing surface conductivity. Specifically, clay minerals enhance surface conductivity through interfacial polarization effects and decrease rock stiffness, which exacerbates wave velocity attenuation. Furthermore, resistivity exhibits a nonlinear negative correlation with water saturation, with sharp increases at low saturation levels due to the disruption of conductive pathways. By integrating the Modified Generalized Effective Medium Theory of Induced Polarization (MGEMTIP) and Kuster–Toksöz models, this study establishes quantitative relationships between porosity, saturation, and electrical/elastic parameters, and constructs cross-plot templates that correlate elastic wave velocities with resistivity and surface conductivity. These analyses reveal that high-porosity, high-saturation zones are characterized by lower resistivity and wave velocities, coupled with significantly higher surface conductivity. The proposed methodology significantly improves the accuracy of reservoir evaluation and enhances fluid identification capabilities, providing a solid theoretical foundation for the efficient exploration and development of tight sandstone reservoirs. Full article
(This article belongs to the Special Issue Electromagnetic Inversion for Deep Ore Explorations)
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18 pages, 2330 KiB  
Article
Adaptive Differential Evolution Algorithm for Induced Polarization Parameters in Frequency-Domain Controlled-Source Electromagnetic Data
by Lei Zhou, Tianjun Cheng, Min Yao, Jianzhong Cheng, Xingbing Xie, Yurong Mao and Liangjun Yan
Minerals 2025, 15(7), 754; https://doi.org/10.3390/min15070754 - 18 Jul 2025
Viewed by 233
Abstract
The frequency-domain controlled-source electromagnetic method (CSEM) has been widely used in fields such as oil and gas and mineral resource exploration. In areas with a significant IP response, the CSEM signals will be modified by the IP response of the subsurface. Accurately extracting [...] Read more.
The frequency-domain controlled-source electromagnetic method (CSEM) has been widely used in fields such as oil and gas and mineral resource exploration. In areas with a significant IP response, the CSEM signals will be modified by the IP response of the subsurface. Accurately extracting resistivity and polarization information from CSEM signals may significantly improve the exploration interpretations. In this study, we replaced real resistivity with the Cole–Cole complex resistivity model in a forward simulation of the CSEM to obtain electric field responses that included both induced polarization and electromagnetic effects. Based on this, we used the adaptive differential evolution algorithm to perform a 1-d inversion of these data to extract both the resistivity and IP parameters. Inversion of the electric field responses from representative three-layer geoelectric models, as well as from a more realistic seven-layer model, showed that the inversions were able to effectively recover resistivity and polarization information from the modeled responses, validating our methodology. The electric field response of the real geoelectric model, with 20% random noise added, was then used to simulate actual measured CSEM signals, as well as subjected to multiple inversion tests. The results of these tests continued to accurately reflect the resistivity and polarization information of the model, confirming the applicability and reliability of the algorithm. These results have significant implications for the processing and interpretation of CSEM data when induced polarization effects merit consideration and are expected to promote the use of the CSEM in more fields. Full article
(This article belongs to the Special Issue Electromagnetic Inversion for Deep Ore Explorations)
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