基于自适应噪声完全集合经验模态分解算法和Hurst指数的地震数据去噪方法

毛世榕 史水平 玉壮基 苏梅艳 李莎 何嘉幸 符衡 张清

毛世榕,史水平,玉壮基,苏梅艳,李莎,何嘉幸,符衡,张清. 2022. 基于自适应噪声完全集合经验模态分解算法和Hurst指数的地震数据去噪方法. 地震学报,45(0):1−12 doi: 10.11939/jass.20210165
引用本文: 毛世榕,史水平,玉壮基,苏梅艳,李莎,何嘉幸,符衡,张清. 2022. 基于自适应噪声完全集合经验模态分解算法和Hurst指数的地震数据去噪方法. 地震学报,45(0):1−12 doi: 10.11939/jass.20210165
Mao S R,Shi S P,Yu Z J,Su M Y,Li S,He J X,Fu H,Zhang Q. 2022. A seismic data denoising method based on complete ensemble empirical mode decomposition with adaptive noise and Hurst exponent. Acta Seismologica Sinica,45(0):1−12 doi: 10.11939/jass.20210165
Citation: Mao S R,Shi S P,Yu Z J,Su M Y,Li S,He J X,Fu H,Zhang Q. 2022. A seismic data denoising method based on complete ensemble empirical mode decomposition with adaptive noise and Hurst exponent. Acta Seismologica Sinica45(0):1−12 doi: 10.11939/jass.20210165

基于自适应噪声完全集合经验模态分解算法和Hurst指数的地震数据去噪方法

doi: 10.11939/jass.20210165
详细信息
  • 中图分类号: P315.61

A seismic data denoising method based on complete ensemble empirical mode decomposition with adaptive noise and Hurst exponent

  • 摘要: 在地震观测中,地震数据中普遍包含有噪声信号。由于噪声信号的干扰,地震分析的效率会受到不同程度影响。传统的去噪方法通常需要噪声的先验知识,并且滤波时会造成部分有效数据丢失。针对这一问题,本文提出一种将自适应噪声完全集合经验模态分解(CEEMDAN)算法与Hurst指数相结合的地震数据去噪方法。首先通过CEEMDAN方法将信号分解为一系列本征模函数(IMF),然后利用Hurst指数对滤波后的IMF分量进行识别,最后对地震数据IMF分量进行重构,从而实现数据去噪。与传统方法的去噪效果对比表明,本文方法对低信噪比波形的滤波性提高32%,对高信噪比波形的滤波性提高6倍。同时对地磁数据的去噪结果表明,本文方法能够较完整地将地铁噪声从地磁信号波形中滤除。

     

  • 图  1  本文算法流程框图

    Figure  1.  A flowchart of this paper

    图  2  事件垂直向原始波形

    Figure  2.  Vertical original waveforms of the events

    图  3  CEEMDAN算法分解结果

    (a)百色地震事件波形;(b)灵川地震事件波形;(c)南宁爆破事件波形;(d)桂林坍塌事件波形

    Figure  3.  Decomposition results of CEEMDAN

    (a) Baise earthquake waveform;(b) Lingchuan earthquake waveform; (c) Nanning blasting waveform;(d) Guilin collapse waveform

    图  4  使用本文方法所得四个事件的去噪结果

    左侧为原始波形,右侧为去噪后波形(a)百色地震事件波形;(b)灵川地震事件波形;(c)南宁爆破事件波形;(d)桂林坍塌事件波形

    Figure  4.  Denoising results of the four events by the method proposed in this paper

    The left column stands for original waveforms and the right column stands for denoising waveforms (a) Baise earthquake waveform;(b) Lingchuan earthquake waveform;(c) Nanning blasting waveform;(d) Guilin collapse waveform

    图  5  不同方法对灵川地震事件(a)和桂林坍塌事件(b)的去噪结果

    Figure  5.  Denoising results of Lingchuan earthquake (a) and Guilin collapse (b) by different methods

    图  6  邕宁地磁台2021年1月1日00时至5日24时观测数据波形

    Figure  6.  Observation waveform at Yongning geomagnetic station from 00:00 on January 1 to 24:00 on January 5,2021

    图  7  地磁数据CEEMDAN分解结果

    Figure  7.  Decomposition results of geomagnetic data by CEEMDAN

    图  8  地磁数据去噪结果

    Figure  8.  Denoising results of geomagnetic data

    表  1  本文所选事件的信息

    Table  1.   The information of events selected in this paper

    事件序号事件类型发震日期震中位置记录台站震中距/kmML
    a天然地震2021-04-28广西百色FUN731.3
    b天然地震 2017-11-25广西灵川YOF1481.9
    c人工爆破 2021-04-04广西南宁DAQ882.0
    d山体坍塌2020-06-23广西桂林GUL362.3
    下载: 导出CSV

    表  2  不同事件IMF分量的Hurst指数

    Table  2.   The Hurst exponents of different IMF components

    分量序号2021年4月28日广西
    百色地震事件
    2017年11月25日广西
    灵川地震事件
    2021年4月4日广西南宁
    人工爆破事件
    2020年6月23日广西桂林
    人工山体坍塌事件
    IMF10.302 50.294 60.258 90.359 4
    IMF20.322 60.407 50.425 50.325 4
    IMF30.307 00.287 50.356 10.466 3
    IMF40.418 90.333 70.306 30.487 1
    IMF50.483 80.384 60.515 90.361 1
    IMF60.655 80.640 80.612 20.647 8
    IMF70.686 00.686 20.658 90.683 1
    IMF80.763 80.792 90.796 60.653 0
    IMF90.806 20.877 70.870 90.864 5
    IMF100.969 30.916 50.992 90.908 0
    IMF110.973 30.966 81.007 00.955 1
    IMF120.985 11.009 21.004 0
    IMF130.994 9
    下载: 导出CSV

    表  3  不同类型去噪方法的质量因子Q值和均方根误差RMSE

    Table  3.   Quality factor Q and root-mean-square error RMSE of different denoising methods

    去噪方法灵川地震事件桂林坍塌事件
    QRMSEQRMSE
    带通滤波器 0.723 9 0.380 1 1.360 1 0.076 1
    小波包变换 0.718 5 0.375 6 1.193 9 0.073 3
    EMD方法 0.854 9 0.374 7 7.344 2 0.040 0
    本文方法 0.955 9 0.373 9 9.606 9 0.025 7
    下载: 导出CSV

    表  4  地磁数据IMF分量的Hurst指数

    Table  4.   The Hurst exponent of different IMFs from geomagnetic data

    序号Hurst指数序号Hurst指数
    IMF10.284 6IMF70.702 9
    IMF20.323 2IMF80.861 3
    IMF30.208 0IMF90.954 7
    IMF40.352 4IMF100.989 9
    IMF50.497 9IMF110.997 1
    IMF60.583 0IMF121.006 3
    下载: 导出CSV
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  • 网络出版日期:  2022-12-13

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