一种小波变换信号处理方法

J4 ›› 2009, Vol. 36 ›› Issue (4): 751-755.

一种小波变换信号处理方法

李传伟^1,2;李安宗¹

(1. 油气资源与勘探技术教育部重点实验室(长江大学)，湖北荆州 434023;
2. 西北工业大学自动化学院，陕西西安 710072)

收稿日期:2008-06-11 出版日期:2009-08-20 发布日期:2009-09-28
通讯作者: 李传伟
基金资助:
油气资源与勘探技术教育部重点实验室(长江大学)资助(2006K002)

Algorithms and application of the wavelet-based signal processing

LI Chuan-wei^1,2;LI An-zong¹

(1. Ministry of Edu. Key Lab. of Exploration Tech. for Oil and Gas Resources(Yangtze Univ.), Jinzhou 434023， China;
2. College of Automation, Northwestern Polytechnical Univ., Xi'an 710072， China)

Received:2008-06-11 Online:2009-08-20 Published:2009-09-28
Contact: LI Chuan-wei

摘要/Abstract

摘要：

针对钻井泥浆脉冲信号在复杂环境中传输的问题，建立了复合泥浆信号模型，研究了符合信号特征、基于最低熵的最优小波设计及处理方法．利用该小波对信号进行变换，分析信号、噪声和基线的模极大值传播特性，提出了模极大值平方后与相邻系数相乘且归一化的处理方法，实现噪声去除、基线矫正和脉冲位置检测．该方法在中国石油某油田得到现场应用，处理采集的钻井泥浆信号，检测脉冲位置恢复原始编码数据，可满足随钻测井的要求．

关键词: 泥浆信号, 小波变换, 模极大值, 去噪, 基线矫正, 突变检测

Abstract:

Confined to the measuring and transmitting conditions in the field, the mud signal model is usually set which contains all kinds of noises. In order to manipulate the signals, an optimal wavelet is designed according to the characteristics of the signals based on the minimum entropy. By using the wavelet, the modular maximum of signals, noises and the baseline are calculated for different scales. The advanced processing algorithms for the modular maximum are conducive to noise-reduction, baseline correction, and singularity detection. The algorithms have been applied with satisfactory results in the Measure While Drilling system in some oil field.

Key words: mud signal, wavelet transforms, modular maximum, noise reduction, baseline correction, singularity detecting

中图分类号:

TP391

李传伟;李安宗. 一种小波变换信号处理方法[J]. J4, 2009, 36(4): 751-755.

LI Chuan-wei;LI An-zong. Algorithms and application of the wavelet-based signal processing[J]. J4, 2009, 36(4): 751-755.

参考文献

［1］Mallat S. A Wavelet Tour of Signal Processing［M］. San Diego: Acacdemic Press, 1998.
［2］Donoho D L. Denoising by Soft-thresholding ［J］. IEEE Trans on Information Theory,1999,1(2):115-22.
［3］Mallat S, Hwang W L. Singularity Detection and Processing with Wavelet［J］. IEEE Trans on IT, 1992, 38(2): 617-643.
［4］Zhang L. Edge Detection by Scale Multiplication in Wavelet Domain［J］. Pattern Recognition Letters, 2002(23): 1771-1784.
［5］Krim H. On Denoising and Best Representation［J］. IEEE Trans on Information Theroy,1999, 45(7): 2225-2238.
［6］张超, 李建军. 基于小波变换的微弱信号的检测与提取［J］. 包头钢铁学院学报, 2006, 25(4): 335-338.
Zhang Chao, Li Jianjun. Detected and Picked-up Life Signals Based on Wavelet Transform［J］. Journal of Baotou University of Iron and Steel Technology, 2006, 25(4): 335-337.
［7］David B H. Ratiionnalizing the Coefficients of Popular Biorthogonal Wavelet Filters［J］. IEEE Trans on Circuits and System for Video Technology, 2000, 10(6): 998-1005.
［8］Sweldens W. The Lifting Scheme: a Construction of Second Generation and Sparsity［J］. SIAM Journal Mathematical Analysis,1997, 29(2): 511-546.
［9］高世伟, 郭雷. 一种最优小波的设计方法［J］. 计算机工程与应用, 2007, 43(23): 86-88.
Gao Shiwei, Guo Lei. Algorithm of Design of Optimal Wavelet Processing［J］. Computer Engineering and Applications, 2007, 43(23): 86-88.
［10］Mallat S, Zhong S. Characterization of Signal from Multi-scale Edges［J］. IEEE Trans on Pattern Analysis and Machine Intelligence, 2003, 14(7): 710-732.
［11］杨玉东, 金魁. 对称小波变换用于非平稳信号检测和去噪［J］. 淮阴工学院学报, 2007, 16(3): 44-48.
Yang Yudong, Jin Kui. On Symmetry Wavelet Transformation Applied to Examine Non-stable Signal and Remove Noise［J］. Journal of Huaiyin Institude of Technology, 2007, 16(3): 44-48.
［12］玄炜, 徐新盛, 吴英姿. 一种小波包分解的FX结构全频带ANC系统［J］. 西安电子科技大学学报, 2008, 35(2): 356-361.
Xuan Wei, Xu Xinsheng, Wu Yingzi. Whole-band ANC System with FX Structure Based on Wavelet Packet Decomposition［J］. Journal of Xidian University, 2008, 35(2): 356-361.
［13］张德丰. 基于小波的信号突变点检测算法研究［J］. 计算机工程与科学, 2007, 29(12): 98-100.
Zhang Defeng. Research on the Wavelet-Based Algorithms for Signal Singularity Detection［J］. Computer engineering ＆ Science, 2007, 29(12): 98-100.
［14］孙延奎. 小波分析及其应用［M］. 北京: 机械工业出版社, 2005.
［15］何坤, 李健. 非平稳环境下基于小波变换的信号去噪［J］. 信号处理, 2005, 21(3): 244-248.
He Kun, Li Jiang. Noise Reduction Based on Wavelet Transform under Non-stationary Environments［J］. Signal Processing, 2005, 21(3): 244-246.
［16］郝琳波, 牛海军, 卢春梅. 融合肤色模型与小波变换人脸识别算法［J］. 西安电子科技大学学报, 2007, 34(6): 864-868.
Hao Linbo, Niu Haijun, Lu Chunmei. Face Detection Algorithm Fusion of Skin-color Model and Wavelet Transform［J］. Journal of Xidian University, 2007, 34(6): 864-868.

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