基于小波降噪的隧道围岩监测数据分析

基于小波降噪的隧道围岩监测数据分析

张鹏¹，李献勇²，陈剑平¹

1.吉林大学建设工程学院，长春 130026;2.台州市诸永高速公路建设指挥部，浙江台州 318000

收稿日期:2008-01-08 修回日期:1900-01-01 出版日期:2008-11-26 发布日期:2008-11-26
通讯作者: 张鹏

Monitoring Data Analysis of Tunnel Surrounding Rock Based on Wavelet Denoising

ZHANG Peng¹, LI Xian-yong²,CHEN Jian-ping¹

1.College of Construction Engineering, Jilin University, Changchun 130026, China;2.Zhuyong Expressway Construction Headquarters of Taizhou Municipal, Taizhou，Zhejiang 318000, China

Received:2008-01-08 Revised:1900-01-01 Online:2008-11-26 Published:2008-11-26
Contact: ZHANG Peng

摘要/Abstract

摘要： 隧道围岩监测数据中含有大量的随机误差，为了消除或削弱随机误差的干扰，通常对观测数据进行降噪处理。基于小波分析理论，利用小波降噪技术，以某隧道的围岩监测数据为例，选择了db3小波函数和heursure软阈值对围岩接触压力进行降噪处理，并用5-15-1BP神经网络对降噪前后的结果进行了预测比较，训练步数分别为2 448步和450步，未降噪的围岩压力预测的误差总体上要比降噪后的误差大。实际计算结果表明，小波去噪合理有效，能够敏感识别观测噪声和有用信息，适合于隧道围岩监测的数据分析。

关键词: 围岩监测, 小波分解, 去噪, Mallat算法

Abstract: There are many random errors in the monitoring data of tunnel surrounding rock. The monitoring data is usually denoised for reducing or eliminating the disturbance of the random errors. Based on the theory of wavelet transform, as an example, the monitoring data of a tunnel surrounding rock is processed by a technique of wavelet denoising with db3 wavelet function and heursure soft threshold. The result of pressure prediction is given by using 5-15-1 BP neural network for the original data and the de-noised data, and the training steps are 2 448 and 450 respectively. The error of the pressure prediction for the original data is larger than that for the de-noised data. The results show that the method of wavelet denoising is efficient and reliable, is sensitive to distinguish noise and useful information, is particularly suitable to analyze monitoring data of surrounding rock.

Key words: monitoring of surrounding rock, wavelet transform, noise reduction, Mallat algorithm

中图分类号:

P642

张鹏，李献勇，陈剑平. 基于小波降噪的隧道围岩监测数据分析[J]. J4, 2008, 38(6): 1010-1014.

ZHANG Peng, LI Xian-yong,CHEN Jian-ping. Monitoring Data Analysis of Tunnel Surrounding Rock Based on Wavelet Denoising[J]. J4, 2008, 38(6): 1010-1014.

参考文献

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