Research on Automatic Correction Methods for Underground Coal Mine Drilling Trajectories Based on Deep Learning

Journal: Architecture Engineering and Science DOI: 10.32629/aes.v6i4.4784

Jianxun Gao¹, Fulong Sun¹, Chunxin Wang², Jingyi Mao², Tingjie Yan²

1. Huaneng Coal Technology Research Co., Ltd., Beijing 100070, China
2. Xinzhuang Coal Mine, Qingyang Xinzhuang Coal Industry Co., Ltd., Qingyang 745300, China

Abstract

This study addresses the complex geological environment in underground drilling by analyzing the influence mechanisms of geological structures, drill string configurations, and rock layer properties on trajectory deviation. A multi-source data fusion prediction model was developed. By introducing a multi-head attention mechanism into an improved Transformer architecture, the system achieves adaptive weight allocation for measurement while drilling, geological parameters, and process parameters, significantly enhancing prediction accuracy. Based on deep reinforcement learning, an Actor-Critic intelligent deviation correction algorithm was designed to establish an automatic trajectory correction system. Field tests demonstrate that the system controls trajectory deviation within ±0.5°, achieving a 42.3% improvement in accuracy and a 35.8% increase in efficiency compared to traditional methods, providing a novel technical approach for precise underground drilling.

Keywords

Underground coal mining drilling; Trajectory deviation correction; Reinforcement learning; Multi-source data fusion

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