Li Jiang, Dai Lei, Xu Keke, Mei Xinyu, Liu Yifu, Shi Jianlin, Zhang Hebing
Yong Coal Company Geological Survey Department, Henan Energy Group, Yongcheng, Henan, China.
School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, Henan, China.
PLoS One. 2025 Jun 23;20(6):e0325913. doi: 10.1371/journal.pone.0325913. eCollection 2025.
To address the complex deformation of wellbores influenced by surrounding coal mining operations, this study employed an improved modified least-squares ambiguity decorrelation (MLAMBDA) algorithm based on the double-difference model for high-frequency dynamic computation of Bei Dou System and Global Navigation Satellite System (BDS/GNSS) observation data. A quantitative analysis was conducted on the performance of various combinations of BDS/GNSS in wellbore deformation monitoring, and the effects of different baseline lengths on the monitoring results were evaluated. Based on the high-precision deformation monitoring sequences, an intelligent early warning model for wellbore deformation was established using the deep learning Bi-LSTM algorithm. The results indicate that the monitoring accuracy of the BDS/GNSS multi-system combination in the E, N and U directions is within 2 mm, with all three directions outperforming the results obtained from a single Global Position System (GPS) system. As the baseline length increased from 1 km to 6 km, the accuracy in the E, N, and U directions decreased by 15.8%, 16.0%, and 5.6%, respectively. Within a 6 km range, the horizontal accuracy remains better than 3 mm, while the vertical accuracy is better than 6 mm, meeting the requirements for wellbore deformation monitoring. The early warning model can flexibly adapt to the deformation conditions at different sites and the various disturbances encountered, effectively capturing the complex nonlinear time-varying characteristics of the observation time series. The prediction of future results for one month based on one year of observation sequences achieves an accuracy better than mm, providing a safeguard for safe production in mines. This research method can also be extended to use BDS/GNSS for hourly level high-precision deformation monitoring and early warning of major engineering infrastructure such as bridges, dams, and high-speed railway systems.
为解决受周边煤矿开采作业影响的井筒复杂变形问题，本研究采用基于双差模型的改进型最小二乘模糊度去相关（MLAMBDA）算法，对北斗系统和全球导航卫星系统（BDS/GNSS）观测数据进行高频动态计算。对BDS/GNSS不同组合在井筒变形监测中的性能进行了定量分析，并评估了不同基线长度对监测结果的影响。基于高精度变形监测序列，利用深度学习双向长短期记忆（Bi-LSTM）算法建立了井筒变形智能预警模型。结果表明，BDS/GNSS多系统组合在E、N和U方向的监测精度在2毫米以内，三个方向均优于单一全球定位系统（GPS）系统的监测结果。随着基线长度从1公里增加到6公里，E、N和U方向的精度分别下降了15.8%、16.0%和5.6%。在6公里范围内，水平精度保持在3毫米以上，垂直精度保持在6毫米以上，满足井筒变形监测要求。预警模型能够灵活适应不同场地的变形条件和遇到的各种干扰，有效捕捉观测时间序列复杂的非线性时变特征。基于一年观测序列对未来一个月的结果预测精度优于毫米级，为矿山安全生产提供了保障。该研究方法还可推广应用BDS/GNSS对桥梁、大坝和高速铁路系统等重大工程基础设施进行小时级高精度变形监测与预警。
