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<p>The extraction of mineralization-related geochemical anomalies through data mining has become a critical component of mineral exploration. With the accumulation of multi-element and high-dimensional geochemical survey datasets, traditional analytical methods have become increasingly inadequate for capturing the complex nonlinear patterns associated with mineralization. Consequently, deep learning algorithms have been applied to geochemical anomaly identification due to their strong capability for nonlinear feature learning and modeling of complex relationships among multiple geochemical variables. The geochemical spatial patterns are strongly controlled by geological processes and commonly exhibit spatial heterogeneity, anisotropy, and directional continuity. Although graph-based representations are well suited for modeling non-Euclidean spatial relationships, conventional static graph construction strategies often struggle to capture the spatial heterogeneity and directional characteristics inherent in geochemical survey data. In addition, static graph neural networks rely on a fixed graph topology throughout the training process, limiting their ability to modify node connectivity based on learned high-level features and to continuously focus on the most important nodes. In order to overcome these limitations, we propose a dynamic graph representation method. By introducing a dynamic edge construction mechanism while retaining the advantages of graph structure, the proposed approach enables edge weights to be adaptively updated according to both spatial distance and node feature similarity during initial graph construction and successive graph convolution operations. The case study conducted in Southwest Fujian Province, China, demonstrates that the proposed method effectively characterizes the spatial heterogeneity of geochemical patterns associated with mineralization, improving the performance of geochemical anomaly identification and interpretability of the obtained results. The identified geochemical anomalies provide valuable targets and geological insights for the next round of mineral exploration in the study area.</p>

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Modeling the Spatial Heterogeneity of Geochemical Patterns Related to Mineralization

  • 期刊论文

  • 发表日期：

    2026年7月25日

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  • Ying Xu,
  • Renguang Zuo

摘要

The extraction of mineralization-related geochemical anomalies through data mining has become a critical component of mineral exploration. With the accumulation of multi-element and high-dimensional geochemical survey datasets, traditional analytical methods have become increasingly inadequate for capturing the complex nonlinear patterns associated with mineralization. Consequently, deep learning algorithms have been applied to geochemical anomaly identification due to their strong capability for nonlinear feature learning and modeling of complex relationships among multiple geochemical variables. The geochemical spatial patterns are strongly controlled by geological processes and commonly exhibit spatial heterogeneity, anisotropy, and directional continuity. Although graph-based representations are well suited for modeling non-Euclidean spatial relationships, conventional static graph construction strategies often struggle to capture the spatial heterogeneity and directional characteristics inherent in geochemical survey data. In addition, static graph neural networks rely on a fixed graph topology throughout the training process, limiting their ability to modify node connectivity based on learned high-level features and to continuously focus on the most important nodes. In order to overcome these limitations, we propose a dynamic graph representation method. By introducing a dynamic edge construction mechanism while retaining the advantages of graph structure, the proposed approach enables edge weights to be adaptively updated according to both spatial distance and node feature similarity during initial graph construction and successive graph convolution operations. The case study conducted in Southwest Fujian Province, China, demonstrates that the proposed method effectively characterizes the spatial heterogeneity of geochemical patterns associated with mineralization, improving the performance of geochemical anomaly identification and interpretability of the obtained results. The identified geochemical anomalies provide valuable targets and geological insights for the next round of mineral exploration in the study area.