- Title
- A Spatial Data-Driven Approach for Mineral Prospectivity Mapping
- Creator
- Senanayake, Indishe P.; Kiem, Anthony S.; Hancock, Gregory R.; Metelka, Václav; Folkes, Chris B.; Blevin, Phillip L.; Budd, Anthony R.
- Relation
- Remote Sensing Vol. 15, Issue 16, no. 4074
- Publisher Link
- http://dx.doi.org/10.3390/rs15164074
- Publisher
- MDPI AG
- Resource Type
- journal article
- Date
- 2023
- Description
- Mineral prospectivity mapping is a crucial technique for discovering new economic mineral deposits. However, detailed knowledge-based geological exploration and interpretations generally involve significant costs, time, and human resources. In this study, an ensemble machine learning approach was tested using geoscience datasets to map Cu-Au and Pb-Zn mineral prospectivity in the Cobar Basin, NSW, Australia. The input datasets (magnetic, gravity, faults, electromagnetic, and magnetotelluric data layers) were chosen by considering their association with Cu-Au and Pb-Zn mineralization patterns. Three machine learning algorithms, namely random forest (RF), support vector machine (SVM), and maximum-likelihood (MaxL) classification, were applied to the input data. The results of the three algorithms were ensembled to produce Cu-Au and Pb-Zn prospectivity maps over the Cobar Basin with improved classification accuracy. The findings demonstrate good agreement with known mineral occurrence points and existing mineral prospectivity maps developed using the weights-of-evidence (WofE) method. The ability to capture training points accurately and the simplicity of the proposed approach make it advantageous over complex mineral prospectivity mapping methods, to serve as a preliminary evaluation technique. The methodology can be modified with different datasets and algorithms, facilitating the investigations of mineral prospectivity in other regions and providing guidance for more detailed, high-resolution geological investigations.
- Subject
- machine learning; maximum-likelihood classification; mineral prospectivity; random forest; support vector machine
- Identifier
- http://hdl.handle.net/1959.13/1491227
- Identifier
- uon:53041
- Identifier
- ISSN:2072-4292
- Rights
- © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
- Language
- eng
- Full Text
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