Electromagnetic Modeling of Geophysical Prospecting in Remote Sensing
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".
Deadline for manuscript submissions: 29 August 2025 | Viewed by 598
Special Issue Editors
Interests: mineral electromagnetic exploration; near-source electromagnetic method; data inversion; semi-airborne electromagnetic survey
Special Issues, Collections and Topics in MDPI journals
Interests: geophysic; multi-physics; joint inversion electromagnetics potential field
Special Issue Information
Dear Colleagues,
Electromagnetic techniques are pivotal in exploring deep geological structures, mineral deposits, and ore field structures, tracing regional ore-forming fluids, studying distinctive mineralization processes, understanding continental geodynamics, exploring concealed mineral deposits, assessing underground water sources, and conducting geological engineering surveys.
This Special Issue, titled "Electromagnetic Modeling of Geophysical Prospecting in Remote Sensing", aims to highlight the latest advancements in the integration of geophysical electromagnetic modeling methods. The issue will cover innovative approaches to applying EM methods for subsurface exploration, with a focus on improving remote sensing data resolution and accuracy. In addition, it will explore the synergy between EM modeling and machine learning techniques.
This Special Issue will include 10–20 high-quality articles, presenting cutting-edge research and practical applications of electromagnetic modeling in remote sensing from diverse perspectives, maintaining the highest standards of scientific rigor.
Key topics of interest include, but are not limited to, the following:
- Development and application of electromagnetic modeling techniques in remote sensing for mineral exploration, groundwater studies, and geohazard monitoring;
- Advanced numerical methods for simulating EM wave propagation in complex media;
- Integration of geophysical data with remote sensing imagery and machine learning algorithms for improved interpretation and anomaly detection;
- Machine learning applications in electromagnetic data inversion, model optimization, and predictive modeling for geological prospecting;
- Case studies showcasing the use of electromagnetic methods in various geophysical applications, including oil and gas exploration, environmental monitoring, and geotechnical investigations.
Prof. Dr. Guoqiang Xue
Dr. Xiaolei Tu
Dr. Hongzhu Cai
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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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 modeling
- geophysical prospecting
- remote sensing
- machine learning in geophysics
- electromagnetic wave propagation
- subsurface exploration
- mineral exploration
- groundwater detection
- environmental monitoring
- inversion methods
- data integration
- computational geophysics
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