A Temporal-Spatial Attention-based Dynamic Correction Method for Geological Model of Coal Seam

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Abstract
The high-accuracy geological model of coal seam is an effective guarantee to realize unmanned mining. To reduce errors in the geological model between static and actual, this paper proposes a temporal-spatial attention-based dynamic correction method for the geological model of the coal seam by combining the cutting trajectory of the shearer with the original geological model of the coal seam. The cutting trajectory provides a reasonable extrapolation basis in the temporal aspect, while the original geological model provides a reference for adjusting the target coal seam in the spatial aspect. The method utilizes a convolutional layer to explore the relationship between the targeted coal seam and adjacent geological models; it also uses a recurrent layer to investigate the direct relationship between the targeted coal seam and the known coal seams. Finally, the attention mechanism is combined to determine the temporal and spatial dependencies of the targeted coal seam automatically. The proposed method was validated using the accurate geological model of coal seams and historical data of the cutting trajectory. The results demonstrate that the proposed method effectively improves the local accuracy of the geological model and enhances its practical value and applicability.
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