| The Application of Wavelet Transform in Analysis of Digital Precursory Observational Data |
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| 作者姓名： | Song Zhiping |
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| 作者单位： | [1]EarthquakeAdministrationofShanghaiMunicipality,Shanghai200062,China [2]EarthquakeAdministrationofBeijingMunicipality,Beijing100081,China [3]EarthquakeAdministrationofShandongProvince,Jinan250014,China |
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| 基金项目： | ThisprojectisoneoftheKeyResearchProjectsduringthe10th“Five YearPlan” (2 0 0 1BA6 0 1B0 1 0 4 0 2 ) ,China |
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| 摘 要： | Digital data of precursors is noted for its high accuracy. Therefore, it is important to extract the high frequency information from the low ones in the digital data of precursors and todiscriminate between the trend anomalies and the short-term anomalies. This paper presents a method to separate the high frequency information from the low ones by using the wavelet transform to analyze the digital data of precursors, and illustrates with examples the train of thoughts of discriminating the short-term anomalies from trend anomalies by using the wavelet transform, thus provide a new effective approach for extracting the short-term and trend anomalies from the digital data of precursors.
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| 关 键 词： | 小波变换 信号变化序列 短时不规则性 数字化处理 地震观测 |
The Application of Wavelet Transform in Analysis of Digital Precursory Observational Data |
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| Song Zhiping.The Application of Wavelet Transform in Analysis of Digital Precursory Observational Data[J].Earthquake Research in China,2004,18(3):225-233. |
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| Authors: | Song Zhiping |
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| Institution: | Song Zhiping 1),Wu Anxu 2),Wang Wei 3),Geng Jie 3),Song Xianyue 1),Ni Youzhong 1),Zhu Jiamiao 1) and Kan Daoling 1) 1)Earthquake Administration of Shanghai Municipality,Shanghai 200062,China 2)Earthquake Administration of Beijing Municipality,Beijing 100081,China 3)Earthquake Administration of Shandong Province,Jinan 250014,China |
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| Abstract: | Digital data of precursors is noted for its high accuracy. Therefore, it is important to extract the high frequency information from the low ones in the digital data of precursors and to discriminate between the trend anomalies and the short-term anomalies. This paper presents a method to separate the high frequency information from the low ones by using the wavelet transform to analyze the digital data of precursors, and illustrates with examples the train of thoughts of discriminating the short-term anomalies from trend anomalies by using the wavelet transform, thus provide a new effective approach for extracting the short-term and trend anomalies from the digital data of precursors. |
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| Keywords: | Wavelet transform Digital data of precursors High and low frequency variation information Trend anomaly and short-term anomaly |
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