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ArcMPM: An ArcEngine-Based Software for Mineral Prospectivity Mapping via Artificial Intelligence Algorithms

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Abstract

Various artificial intelligence (AI) algorithms have been employed successfully to map mineral prospectivity for a specific mineral deposit type to assist mineral exploration. Numerous tools have been developed to incorporate AI algorithms, such as ArcSDM and ArcGIS. However, existing tools remain inadequate for geologist-friendly functions, and they are not fully tailored for mineral prospectivity mapping (MPM). This limitation has impeded the advancement and utilization of AI algorithms in MPM. Thus, this study introduced a novel ArcEngine-based software named ArcMPM to expeditiously integrate multi-source prospecting information for MPM using AI algorithms. ArcMPM was developed using Python and C#, based on ArcEngine and Visual Studio 2012, which incorporate two popular machine learning (ML) approaches: random forests (RFs) and convolutional neural networks (CNNs), representing shallow ML and deep learning algorithms, respectively. Moreover, it encompasses a complete procedure suitable for MPM by utilizing the RF and CNN models from sample generation to model evaluation. A case study in the Baguio region of the Philippines illustrated the convenience and effectiveness of utilizing ArcMPM for MPM. The success-rate curves demonstrated that the RF and CNN models developed in ArcMPM, particularly the CNN, exhibited high accuracy in delineating high-prospectivity areas. In addition, the case study proved that, in contrast to other GIS tools, ArcMPM can conveniently generate positive and negative samples under geological constraints, customize the model structure to suit the MPM according to the needs of geologists, and provide evaluation metrics that are accessible and practical to geologists.

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Acknowledgments

We are grateful for three reviewers’ comments and suggestions which helped us improve this study. This study was supported by the Natural Science Foundation of Hubei Province (China) (2023AFA001), the National Natural Science Foundation of China (42172326, 42321001), and the Henan Province Key Research and Development Special Fund (221111320600).

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  1. State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan, 430074, China

    Renguang Zuo, Fanfan Yang, Ying Xu & Yihui Xiong

  2. School of Computer Science, China University of Geosciences, Wuhan, 430074, China

    Luyi Shi

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  1. Renguang Zuo
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Zuo, R., Shi, L., Yang, F. et al. ArcMPM: An ArcEngine-Based Software for Mineral Prospectivity Mapping via Artificial Intelligence Algorithms. Nat Resour Res 33, 1–21 (2024). https://doi.org/10.1007/s11053-023-10286-1

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