基于经验小波变换的地震资料噪声压制方法

doi:10.3969/j.issn.1672-7703.2018.05.013

中国石油勘探 ›› 2018, Vol. 23 ›› Issue (5): 100-110.DOI: 10.3969/j.issn.1672-7703.2018.05.013

• 工程技术 • 上一篇

基于经验小波变换的地震资料噪声压制方法

覃发兵¹, 徐振旺², 啜晓宇³, 张小明⁴, 郭乃川⁵, 董玉文⁶, 陈伟⁷

1 长江大学管理学院;
2 中国石油辽河油田公司勘探开发研究院;
3 河北煤炭科学研究院;
4 中国石油集团东方地球物理公司大港物探处;
5 中海石油(中国)有限公司天津分公司渤海石油研究院;
6 中国石油集团东方地球物理公司研究院资料处理中心;
7 非常规油气湖北省协同创新中心

收稿日期:2017-11-22 修回日期:2018-07-19 出版日期:2018-09-15 发布日期:2018-09-15
通讯作者: 陈伟(1985-),男,湖北监利人,博士,2014年毕业于中国石油大学(北京),副教授,现主要从事地震信号处理与解释方面的教学和科研工作。地址:湖北省武汉市蔡甸区大学路111号,邮政编码430100。E-mail:chenwei2014@yangtzeu.edu.cn
基金资助:
国家自然科学基金项目“基于经验模态分解的自由表面多次波衰减方法研究”（41804140）。

Seismic noise suppression based on empirical wavelet transformation

Qin Fabin¹, Xu Zhenwang², Chuan Xiaoyu³, Zhang Xiaoming⁴, Guo Naichuan⁵, Dong Yuwen⁶, Chen Wei⁷

1 School of Management, Yangtze University;
2 Research Institute of Exploration and Development, PetroChina Liaohe Oilfield Company;
3 Hebei Coal Science Research Institute;
4 BGP Dagang Division, CNPC;
5 Bohai Petroleum Research Institute, Tianjin Branch of CNOOC Ltd.;
6 Seismic Data Processing Center of GRI, BGP, CNPC;
7 Hubei Cooperative Innovation Center of Unconventional Oil and Gas

Received:2017-11-22 Revised:2018-07-19 Online:2018-09-15 Published:2018-09-15
Supported by:

摘要/Abstract

摘要： 噪声压制是地震资料处理中重要的环节，目前已有的去噪技术存在着噪声去除不干净、有效信号丢失、不能处理非线性非平稳信号等问题。经验小波变换（Empirical Wavelet Transform，简写为EWT）是一种能自适应分解原始信号的算法，其相较于经典的经验模态分解（Empirical Mode Decomposition，简写为EMD）具有更好的自适应性和完善的数学理论基础。将EWT算法引入到地震资料噪声压制中，选取合适的小波函数并利用EWT算法对目标地震信号进行自适应分解，得到其各个频率尺度的固有模态分量；然后根据原始地震信号的主频设定阈值范围，选取主频值在阈值范围内的固有模态分量进行重构，最终获取去噪后的地震信号。结果表明将EWT噪声压制算法应用于数值模型和实际地震资料中，可以很好地实现有效信号和噪声的分离，结果均比常规算法的去噪效果要好。

关键词: 经验模态分解(EMD), 总体经验模态分解(EEMD), 经验小波变换(EWT), 固有模态分量(IMF), 去噪

Abstract: Noise suppression is an important step in seismic data processing. However, the applicable denoising methods cannot remove noise effectively, or process nonlinear or unstable signals. The Empirical Wavelet Transform (EWT) developed recently is an adaptive decomposition algorithm which has better adaptability and more complete mathematical theory than the Empirical Mode Decomposition (EMD). The EWT algorithm is introduced into seismic data for noise suppression. Firstly, a suitable wavelet function is selected which adaptively decomposes target seismic signals to get intrinsic mode functions at different frequency scales. Secondly, a threshold range is set based on the dominant frequency and the intrinsic mode functions whose dominant frequencies are within the threshold range are selected to reconstruct signals and finally get denoised signals. Application of the EWT algorithm to numerically simulated data and real seismic data has proved effective separation of signals from noises, and the result is better than those from conventional denoising algorithms.

Key words: empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), empirical wavelet transformation (EWT), intrinsic mode function (IMF), denoising

中图分类号:

覃发兵, 徐振旺, 啜晓宇, 张小明, 郭乃川, 董玉文, 陈伟. 基于经验小波变换的地震资料噪声压制方法[J]. 中国石油勘探, 2018, 23(5): 100-110.

Qin Fabin, Xu Zhenwang, Chuan Xiaoyu, Zhang Xiaoming, Guo Naichuan, Dong Yuwen, Chen Wei. Seismic noise suppression based on empirical wavelet transformation[J]. China Petroleum Exploration, 2018, 23(5): 100-110.

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基于经验小波变换的地震资料噪声压制方法

Seismic noise suppression based on empirical wavelet transformation

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