利用小波变换方法分析形变观测资料的正常背景变化特征
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摘要
为定量刻画数字化形变观测资料中背景信息和噪声的时 频分布特征, 本文应用二进小波变换方法, 通过对小波分解的主模特征和随机白噪声识别因子变化特征分析, 剖析了山东数字化形变观测资料的正常动态背景和噪声变化规律. 结果表明, 当尺度取 2, 3 和 4 时,分解后的细节部分存在着1/4日波、半日波、日波和半月波等准循环周期信号, 其中尤以尺度为3时的信号波幅最大; 尺度取1和5时的细节部分主要包含着噪声; 通过分析和追踪指定尺度的数字化形变观测资料小波变换的非震异常特征变化, 可望捕捉到与强地震孕育过程有关的前兆异常信息.
Wavelet transform method is applied to measure time frequency distribution characteristics of digital deformation data and noise. Based on the characteristics of primary modulus and stochastic white noise discrimination factor of wavelet decomposition, we analyze the variation rule of normal background and noise data from Shandong digital deformation observation data. The research results indicate that: a) 1/4 daily wave, semi diurnal tide wave, daily wave and half lunar wave and so on quasi periodic signal exist in the detail decomposing signal of wavelet when scale are equal to 2, 3 and 4; b) The amplitude of detail decomposing signal is the biggest when scale is equal to 3; c) The detail decomposing signal contain mainly noise corresponding to scale 1 and 5, respectively; d) We may trace the abnormal precursory which is related to earthquake by analyzing non earthquake detail wavelet decomposing signal whose scale is specified from digital deformation observation data.
Wavelet transform method is applied to measure time frequency distribution characteristics of digital deformation data and noise. Based on the characteristics of primary modulus and stochastic white noise discrimination factor of wavelet decomposition, we analyze the variation rule of normal background and noise data from Shandong digital deformation observation data. The research results indicate that: a) 1/4 daily wave, semi diurnal tide wave, daily wave and half lunar wave and so on quasi periodic signal exist in the detail decomposing signal of wavelet when scale are equal to 2, 3 and 4; b) The amplitude of detail decomposing signal is the biggest when scale is equal to 3; c) The detail decomposing signal contain mainly noise corresponding to scale 1 and 5, respectively; d) We may trace the abnormal precursory which is related to earthquake by analyzing non earthquake detail wavelet decomposing signal whose scale is specified from digital deformation observation data.
引文
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高静怀,汪文秉,朱光明,等.1996.地震资料处理中小波函数的选取研究[J].地球物理学报,39(3):392~400
侯遵泽,杨文采.1997.中国重力异常的小波变换与多尺度分析[J].地球物理学报,40(1):85~95
蒋骏,李胜乐,张雁滨,等.2000.地震前兆信息处理与软件系统(EIS2000)[M].北京:地震出版社,30~94
敬少群,王佳卫,童迎世.2002.小波变换在少震、弱震区地下水位数据分析中的应用[J].华南地震,22(2):9~15
刘希强,周蕙兰,沈萍,等.2000.用于三分向记录震相识别的小波变换方法[J].地震学报.22(2):125~131
张贤达,保铮.1995.非平稳信号分析与处理[M].北京:国防工业出版社,307~322
张永志,丁平,王继英,等.1997.河西重力变化的小波分解与地震关系研究[J].地壳形变与地震,17(3):27~32
张永志,王双绪.1998.河西地区地壳垂直形变的小波分析结果与中强震关系研究[J].地震学报,20(2):150~157
张永志,王文萍.1999.华北定点形变的小波分析与地震活动研究[J].地壳形变与地震,18(3):48~54
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