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| Citation: | Liu Qiliang, Chen Yuxuan, Liu Zhankun, Mao Xiancheng, Deng Min, 2026. Deep Learning Aided Kalman Filter for 3D Detailed Modelling of Deep Fault in Metal Mines: A Case Study from the Xiadian Gold Deposit, Jiaodong Peninsula, Eastern China. Earth Science, 51(3): 940-954. doi: 10.3799/dqkx.2026.028
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The construction of fine-scale 3D models of deep structures remains challenging due to the lack of direct exploration data and the high uncertainty associated with geophysical prospecting inferred data. To address these issues, utilizing prior knowledge to mitigate the limitations of scarce exploration data and uncertain geophysical data is a valid idea. In this work, a 3D refined modelling method for deep fault named Deep Learning Aided Kalman Filter (DLAKF) was proposed. Based on the concept of Kalman filtering, the 3D modelling of the deep fault from shallow to deep was regarded as a "temporal sequence prediction" problem involving both system disturbances and observation errors: (1) A state equation for the Kalman filter was constructed to predict deep fault positions. The prior knowledge constraints of shallow fault locations and occurrence were integrated in the equation. (2) A deep spatial attention convolutional network, embedded with prior knowledge constraints, was designed. The observation equation for Kalman filter was construct based on the outputs of the deep neural network. By calculating the Kalman Gain, the positions predicted by the state equation and the observation equation were dynamically fused and the optimal estimation of deep fault location was achieved. The proposed method was applied to construct a 3D detailed model of deep fault at the Xiadian gold deposit. DLAKF successfully constructed the detailed model down to 3000 meters. The average horizontal error between the constructed model and drilling holes was 6.17 meters. The accuracy was improved by 91% to 93% compared to existing implicit modelling methods, which means the detailed model constructed by DLAKF reflected the detailed geometry of the deep fault structures more accurately. Based on the reconstructed 3D deep fault model, four prospective mineralization areas were identified in the deep sections of the Xiadian deposit, providing valuable guidance for deep resource exploration.
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