基于Shearlet变换的自适应阈值地震数据去噪方法

doi:10.7623/syxb201801007

石油学报 ›› 2018, Vol. 39 ›› Issue (1): 82-91.DOI: 10.7623/syxb201801007

基于Shearlet变换的自适应阈值地震数据去噪方法

程浩, 陈刚, 王恩德, 侯振隆, 付建飞

东北大学深部金属矿山安全开采教育部重点实验室东北大学资源与土木工程学院辽宁沈阳 110819

收稿日期:2017-05-09 修回日期:2017-11-06 出版日期:2018-01-25 发布日期:2018-03-09
通讯作者: 程浩,男,1988年10月生,2011年获吉林大学学士学位,2016年获吉林大学地球探测科学与技术学院博士学位,现为东北大学资源与土木工程学院讲师,主要从事被动源数据处理、多次波压制等方面的研究工作。Email:chenghao@mail.neu.edu.cn
作者简介:程浩,男,1988年10月生,2011年获吉林大学学士学位,2016年获吉林大学地球探测科学与技术学院博士学位,现为东北大学资源与土木工程学院讲师,主要从事被动源数据处理、多次波压制等方面的研究工作。Email:chenghao@mail.neu.edu.cn
基金资助:
中央高校基本科研业务专项资金资助项目（N160103001，N160103003）和国家重点研发计划项目（2016YFC0801603）资助。

Seismic data de-noising method of adaptive threshold based on Shearlet transform

Cheng Hao, Chen Gang, Wang Ende, Hou Zhenlong, Fu Jianfei

Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, Northeastern University;School of Resources & Civil Engineering, Northeastern University, Liaoning Shenyang 110819, China

Received:2017-05-09 Revised:2017-11-06 Online:2018-01-25 Published:2018-03-09

摘要/Abstract

摘要： 由于随机噪声的干扰，地震勘探的有效信号经常淹没其中难以识别，且在时间域难以分离随机噪声和有效信号。Shearlet变换是一种新的多尺度多方向时频分析方法，具有最优的稀疏表示能力、局部化特征和方向敏感性。Shearlet变换在去除随机噪声的同时，能够最大限度地保留有效信号，可以有效地提高地震数据的信噪比。针对传统的Shearlet变换阈值去噪方法不能随尺度和方向变化的不足，提出了随尺度和方向变化的自适应阈值，可以同时适应不同尺度和方向噪声水平的差异。利用Shearlet变换的自适应阈值算法与小波变换去噪方法，分别对理论和实际地震数据进行去噪。对比可知，Shearlet变换的自适应阈值算法具有更强的去噪能力，并能够最大限度地保留有效信号。

关键词: Shearlet变换, 自适应阈值, 随机噪声, 稀疏性, 信噪比

Abstract: Due to the interference of stochastic noise, the effective signal of seismic exploration is usually covered and thus is difficult to be identified; meanwhile, it is difficult to distinguish the stochastic noise and effective signal in the time domain. As a new type of multi-scale and multi-directional time-frequency analysis method, Shearlet transform possesses the optimal sparse representation capacity, localization characteristics and directional sensibility. When the stochastic noise is removed by Shearlet transform, effective signals can be retained to the maximum degree, and the signal-to-noise ratio of seismic data can be effectively improved. Aiming at the deficiency in traditional Shearlet transform threshold de-noising method unable to change with the scale and direction, the adaptive threshold changing with the scale and direction is proposed, able to adapt to the difference in noise levels of different scales and directions. The adaptive threshold algorithm based on Shearlet transform and de-noising method based on wavelet transform can be used to de-noise the theoretical and actual seismic data respectively. It can be known by contrast that the adaptive threshold algorithm based on Shearlet transform has the stronger de-noising capacity, able to reserve the effective signal to the utmost extent.

Key words: Shearlet transform, adaptive threshold, stochastic noise, sparsity, signal-to-noise ratio

中图分类号:

P315.6

程浩, 陈刚, 王恩德, 侯振隆, 付建飞. 基于Shearlet变换的自适应阈值地震数据去噪方法[J]. 石油学报, 2018, 39(1): 82-91.

Cheng Hao, Chen Gang, Wang Ende, Hou Zhenlong, Fu Jianfei. Seismic data de-noising method of adaptive threshold based on Shearlet transform[J]. Acta Petrolei Sinica, 2018, 39(1): 82-91.

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基于Shearlet变换的自适应阈值地震数据去噪方法

Seismic data de-noising method of adaptive threshold based on Shearlet transform

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