AI-Based GIS for Pinpointing Mineral Deposits
A special issue of Minerals (ISSN 2075-163X). This special issue belongs to the section "Mineral Exploration Methods and Applications".
Deadline for manuscript submissions: closed (17 March 2023) | Viewed by 10863
Special Issue Editors
Interests: geochemical exploration; remote sensing; geomatics; geological mapping; mineral exploration; mining
Special Issues, Collections and Topics in MDPI journals
Interests: multidimensional mineral prospectivity modelling; geological remote sensing; data science in mineral exploration
Special Issues, Collections and Topics in MDPI journals
Interests: deep learning; remote sensing; mineral exploration; environmental and climate sciences
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
With a dwindling in the number of grassroots exploration opportunities, modern-day exploration campaigns are mostly focused on exploring deep-seated, blind, or even covered mineral deposits. However, discovering such mineral deposits is a challenge, given that these are marked by intricate geochemical, geophysical, and geological patterns. Artificial intelligence (AI)-based techniques can help in extracting the subtle patterns in geoscientific data that are linked to the mineralization of the type being sought. In essence, two- and three-dimensional geochemical, geological, and geophysical signatures that are spatially, temporally, and perhaps genetically linked to mineralization should be considered for mineral exploration.
In addition, individual surveys only reveal limited information on mineralization, meaning that mineralization-related signatures outlined by individual surveys should be combined for pinpointing mineral deposits. Developing an AI-aided 4D-geographical information system (GIS), namely a system enabling the analysis, visualization, and integration of 2D- and 3D-based big data concerning their spatial–temporal association with mineralization, is required to discover deep-seated mineral deposits.
Notwithstanding the advancements in geomatics and AI-based algorithms, be they machine- or deep-learning techniques, little has been done to apply these methods in mineral exploration. There is, therefore, a tangible knowledge in the aspects mentioned above that merits further consideration. This Special Issue seeks to cover this knowledge gap by collecting papers on the following topics:
- Machine- and deep-learning-based geochemical and geophysical pattern recognition for mineral exploration
- Machine- and deep-learning-based mineral prospectivity mapping (MPM)
- Novel algorithms for MPM
- Quantification of uncertainty in 2D/3D-based MPM
Dr. Mohammad Parsa
Dr. Ehsan Farahbakhsh
Dr. Rohitash Chandra
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
- geographic information system
- mineral prospectivity mapping
- anomaly detection
- machine learning
- deep learning
- uncertainty