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小波及小波包消噪方法在山西北部水准数据处理中的应用
引用本文:马金平,张青玄,曹文强,侯玉文,贾海玉,靳玉科. 小波及小波包消噪方法在山西北部水准数据处理中的应用[J]. 山西地震, 2013, 0(4): 23-30
作者姓名:马金平  张青玄  曹文强  侯玉文  贾海玉  靳玉科
作者单位:[1]山西省地震局代县中心地震台,山西代县034200 [2]山西省地震局太原基准地震台,山西太原030025 [3]太原大陆裂谷动力学国家野外科学观测研究站,山西太原030025
摘    要:文中简要介绍小波及小波包去噪的基本原理,并对代县台短水准观测数据的小波及小波包阈值去噪曲线进行对比分析。采用Matlab小波包消噪方法对山西北部水准历史资料进行处理,发现小波包消噪后的曲线既可以保留多的有效信号,又可使曲线形态更有利于异常特征分析。同时,将处理后得到的小波包消噪曲线结合长趋势观测曲线对主要震例进行分析,结果显示,周边4级以上地震前,异常对应明显,地震一般发生在水准消噪曲线低值异常时。

关 键 词:随机噪声  小波及小波包  相关阀值  水准资料

The Methods of Wavelet and Wavelet Packet De-noising Applied in Level Data Processing in North of Shanxi Province
MA Jin-ping,ZHANG Qing-xuan,CAO Wen-qiang,HOU Yu-wen,JIA Hai-yu,JIN Yu-ke. The Methods of Wavelet and Wavelet Packet De-noising Applied in Level Data Processing in North of Shanxi Province[J]. Earthquake Research in Shanxi, 2013, 0(4): 23-30
Authors:MA Jin-ping  ZHANG Qing-xuan  CAO Wen-qiang  HOU Yu-wen  JIA Hai-yu  JIN Yu-ke
Affiliation:1. Daixian Central Seismological Station of Earthquake Administration of Shanxi Province, Daixian, Shanxi 034200, China; 2. Taiyuan Referential Seismological Station of Earthquake Administration of Shanxi Province, Taiyuan, Shanxi 030025, China; 3. National Continental Rift Valley Dynamics Observatory of Taiyuan, Taiyuan, Shanxi 030025, China)
Abstract:The basic principle of wavelet and wavelet packet de -noising is briefly introduced .The wavelets of short leveling data at Daixian Station and the data after wavelet packet threshold de -noising are compared.Applying Matlab wavelet packet de-noising, the historical data of leveling in North of Shanxi are processed .It is found that most effective signal still remain in data after wavelet packet de -noising process-ing and become easier to analyze its abnormal feature .Furthermore,through analyzing the characteristics of data after wavelet packet de -noi-sing with the data of long trend observation for some important earthquakes , it is concluded that before earthquakes more than M40.in pe-riphery, abnormities are quite obvious and earthquakes occur generally at the low value abnormities of the level de -noising curves.
Keywords:Random noise  Wavelet and wavelet packet  Corelated threshold  Leveling data
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