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| Citation: | Li Feng, Wang Gongwen, Lu Ziyang, Fu Chao, Liu Yang, Dong Yulong, Gong Tianyi, Zhang Zhiqiang, 2026. Three-Dimensional (3D) Lithological Modeling via Hybrid Attentional Mechanism Deep Learning Model: A Case Study of Jiaojia Gold Field. Earth Science, 51(3): 955-969. doi: 10.3799/dqkx.2025.286
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Enhancing the transparency of deep geological structures at the ore-field scale is critical for subsurface mineral exploration and prospectivity modeling, and three-dimensional (3D) lithological modeling serves as a critical technology for this objective. However, existing ore-field-scale modeling workflows rely on explicit modeling approaches with relatively low efficiency, which can hardly meet the demands of multi-stage mineral exploration and real-time mining. Consequently, high-precision and high-efficiency implicit 3D lithological modeling methods are urgently needed. To address this issue, a Hybrid Attentional Mechanism deep learning model (HAM) is constructed on the basis of the 3D Convolutional Neural Network (3D CNN), integrating the Convolutional Block Attention Module (CBAM) and the Self-Attention Module (SAM). Based on this algorithm, deep representations within multi-source geological and geophysical data are mined to determine the boundaries of geological bodies required for modeling, thereby achieving a 3D lithological implicit modeling method capable of capturing both local details and long-range dependencies. To validate the effectiveness of the proposed hybrid attentional mechanism model, the Jiaojia gold field in the Jiaodong Peninsula was selected as the study area, and comparative and ablation experiments were conducted. Relative to baseline models‒Random Forest (RF) and a vanilla 3D-CNN, HAM markedly improves the macro-averaged accuracy, precision, recall, macro-averaged F1 score and confusion matrix of ore-field-scale implicit 3D lithological modeling, with direct implications for subsurface mineral exploration and mining operations.
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