Advanced Methods of Inversion of Potential Field Data for Mineral Exploration
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 July 2026 | Viewed by 319
Special Issue Editors
2. Department of Geology and Geophysics, University of Utah, Salt Lake City, UT 84112, USA
Interests: magnetics; magnetization vector inversion; gravity; potential fields; inversion; 3D inversion; joint inversion
Interests: theoretical and applied geophysics; inverse theory; joint inversion; mineral exploration; electromagnetic, gravity, magnetic, and seismic methods
Special Issue Information
Dear Colleagues,
Recent advances in geophysical inversion have transformed the way mineral explorers interpret gravity, gravity gradiometry, magnetic, and magnetic gradiometry data. This Special Issue of Minerals seeks to showcase state-of-the-art developments in inversion algorithms, model regularization, and the integration of geological and petrophysical constraints for potential field data. We welcome contributions that explore the use of machine learning or AI, deterministic or probabilistic inversion, joint or cooperative inversion frameworks, and the incorporation of remanent magnetization and anisotropy into 3D models. Case studies demonstrating the practical impact of these methods in identifying and characterizing mineral systems—such as iron oxide–copper–gold (IOCG), nickel–copper–PGE, banded iron formations, porphyry, and lithium-bearing pegmatites or brines—are especially encouraged.
The objective of this Special Issue is to provide a comprehensive platform for researchers and industry professionals to exchange innovative approaches that improve the resolution, reliability, and geological realism of inversion results. By emphasizing methodological rigor and real-world applicability, the Special Issue aims to bridge the gap between theoretical advances and mineral exploration workflows. We invite original papers that highlight the evolution of potential field inversion from classical deterministic frameworks to next-generation, data-integrative models capable of addressing the complexity of modern exploration challenges.
Dr. Michael Jorgensen
Prof. Dr. Michael S. Zhdanov
Guest Editors
Manuscript Submission Information
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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.
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Keywords
- geophysical inversion
- 3D inversion
- mineral exploration
- machine learning/ AI
- potential field data
- joint inversion
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