Mineral Prospectivity Mapping (MPM) Using Multi-Source Datasets, Geo-Statistical Algorithms and Machine Learning Techniques

Dear Colleagues,

Mineral prospectivity mapping (MPM) is one the most important approaches to mineral exploration using a multi-source dataset. It normally uses a multivariable decision-making contrivance to define and highlight potential zones of ore mineralizations in metallogenic areas. MPM is a vital concern in  mineral exploration and mining to diminish the exploration costs by offering an outline of drilling high potential zones of ore mineralizations. Remote sensing, geological, geophysical, and geochemical datasets can be combined to generate a mineral potential map of a study area at local to regional scales. Fusion, analysis, and the selection of information layers using geo-statistical algorithms and machine learning techniques provide a vital phase on the way to accomplishing accurate MPM for mineral exploration in metallogenic provinces. The main goal of this Special Issue is to focus on miscellaneous ideas about data fusion for MPM with a focus on elevating integration methods and intensifying methods for mineral exploration. Multidisciplinary innovative studies of mineral exploration established on a variety of datasets, algorithms, field, and laboratory techniques covering different research aspects to address ore mineral exploration are highly welcome and encouraged.

The topics of interest include but are not limited to

• Remote sensing analysis of multispectral and hyperspectral imagery for mineral exploration;
• Fusion of remote sensing, geophysical, geological, and geochemical datasets;
• GIS and remote sensing integration for mineral exploration modeling;
• Reflectance spectroscopy and geochemistry of rocks and minerals for mineral potential mapping;
• Interpretation and fusion of ASD spectroscopy and XRD, XRF, and ICP-MS analysis for mineral exploration;
• Recent advances in multi-source remote sensing information fusion for mineral exploration;
• Machine learning techniques for integrating remote sensing, geophysical, geological, and geochemical data;
• Multivariate, compositional, and geo-statistical techniques for mineral prospectivity mapping (MPM);
• Delineation of weak geochemical and geophysical anomalies pertaining to blind or covered deposits;
• Three-dimensional modeling of geochemical and geophysical anomalies.

Dr. Amin Beiranvand Pour
Prof. Dr. Ardeshir Hezarkhani
Dr. Aref Shirazi
Dr. Adel Shirazy
Guest Editors

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