Abstract
Mineral prospectivity mapping (MPM) aims to reduce the areas for searching of mineral deposits. Various statistical models that have been successfully adopted to delineate prospecting regions for a specific type of mineral deposit can be divided into pixel-wise, image- (or pixel-patch), and graph-based approaches. The pixel-wise models, which frequently integrate multiple prospecting information (or evidence layers) at a single pixel, do not adequately consider the spatial associations among neighboring pixels and may ignore the spatial patterns linked to mineralization or the spatial distribution characteristics of mineral deposits to some extent. Image-based models such as convolutional neural networks (CNNs) can extract local meaningful features and capture the spatial patterns of prospecting information in MPM because the input data of image-based models are images composed of regular pixels in the Euclidean space. However, CNNs also have limitations in MPM, such as the requirement for regular input data and non-rotationally invariant spatial features. Graphs that are typically composed of nodes and edges have a strong abstraction to capture the complex and nonlinear spatial relationships among multiple nodes and their edges. Prospecting information or evidence layers can be regarded as graphs in which pixels are connected by their adjacent pixels. In this study, graph deep learning algorithms, including graph convolutional networks and graph attention networks, were employed to produce mineral potential maps. A comparative study of graph deep learning algorithms with a CNN demonstrated the advantage of graph deep learning algorithms for MPM in terms of the cumulative areas versus the cumulative number of mineral deposits and the true/false prediction rate plot. These results suggest that the graph-based models, such as graph neutral networks, can effectively capture mineralization information and the spatial interrelations between mineralization and prospecting information.
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Acknowledgements
We are grateful for two reviewers’ comments and suggestions, which improved this paper. This study was supported by the National Natural Science Foundation of China (Nos. 41972303 and 42172326).
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Zuo, R., Xu, Y. Graph Deep Learning Model for Mapping Mineral Prospectivity. Math Geosci 55, 1–21 (2023). https://doi.org/10.1007/s11004-022-10015-z
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DOI: https://doi.org/10.1007/s11004-022-10015-z