基于小波去噪的分布式光纤形变监测预警优化研究  

Early Warning Optimization of Distributed Optical Fiber in Deformation Monitoring Based on Wavelet Denoising

作　　者：张玉芳 杨忠民 李健 任义 ZHANG Yufang;YANG Zhongmin;LI Jian;REN Yi(Railway Engineering Research Institute,China Academy of Railway Sciences Corporation Limited,Beijing 100081;State Key Laboratory for Track System of High-Speed Railway,Beijing 100081;School of Earth and Space Sciences,Peking University,Beijing 100871;China Academy of Safety Science and Technology,Beijing 100012)

机构地区：[1]中国铁道科学研究院集团有限公司铁道建筑研究所,北京100081 [2]高速铁路轨道系统全国重点实验室,北京100081 [3]北京大学地球与空间科学学院,北京100871 [4]中国安全生产科学研究院,北京100012

出　　处：《北京大学学报(自然科学版)》2025年第1期86-98,共13页Acta Scientiarum Naturalium Universitatis Pekinensis

基　　金：广东省交通集团重点研发项目(JT2023YB27)资助。

摘　　要：提出一种基于噪声数据特征确定预警阈值的方法,并将该方法应用于分析隧洞单次和总应变监测数据在时间和空间尺度上的潜在风险。首先基于模拟测试,对小波去噪方法的有效性进行验证;然后在隧洞现场布设千米级长度的分布式光纤进行应变监测,并将小波变换应用于监测数据的去噪分析中。结果显示,小波去噪可以较好地还原有效信号,基于噪声数据特征确定的预警阈值能够更高效地识别风险监测点位,在较大程度上降低无效预警发生的概率。人工现场排查发现的两处洞体破坏位置与基于光纤监测数据分析获得的风险点位较为一致,在一定程度上验证了通过分析得到的风险点位的准确性。同时,结合隧洞及山体的人工测斜和深部位移监测数据分析结果,发现所监测隧洞其赋存坡体存在整体持续缓慢蠕变及局部突变的现象,具有一定的风险性,建议加强监测,并进一步采取防范措施,保证线路运营安全。研究结果可为将小波去噪方法应用于从工程监测的海量数据中提取有效数据以及将分布式光纤应用于各类岩土体的形变监测预警等工作提供参考。A method for determining alarm thresholds based on noise data characteristics is proposed,and this threshold is applied to analyze the potential alarm distribution of single/total strain monitoring data on temporal and spatial scales.Firstly,simulation tests are conducted to verify the effectiveness of wavelet denoising.Then,distri-buted optical fibers with a length of kilometer are laid at the tunnel for strain monitoring.Wavelet transform is applied in denoising analysis of distributed fiber-optic monitoring data in the tunnel.The results show that wavelet denoising can effectively restore effective signals.The warning threshold determined based on the characteristics of noise data can identify risk monitoring points more efficiently and reduce the probability of invalid alarms to a large extent.Moreover,the two locations of tunnel damage discovered through manual investigation are consistent with the risk points obtained based on fiber optic monitoring data analysis,partially verifying the accuracy of the risk points.At the same time,combined with the analysis results of the monitoring data from the tunnel and slope borehole inclinometers,it is found that the tunnel and the existing slopes have overall continuous slow creep and local sudden changes,which are risky.Strengthening monitoring and taking further preventive measures to ensure the safety of line operations are suggested.This research provides further reference for the application of wavelet denoising to extract effective data from massive data in engineering monitoring and the application of distributed optical fiber in deformation monitoring and early warning of various types of rock and soil engineering.

关 键 词：小波去噪 分布式光纤 隧洞变形 风险点位预警 

分 类 号：TP212[自动化与计算机技术—检测技术与自动化装置]