蒋康康^1,,
曹思远^2,,,
孙晓明²,
王航²,
王浩²
1. 中铁第一勘察设计院集团有限公司, 陕西西安 710043
2. 中国石油大学(北京)油气资源与探测国家重点实验室, 北京 102249

基金项目: 国家重点研发计划"高性能计算"专项（2017YFB0202902）、国家自然基金"含裂隙介质AVF规律研究"（41674128）、中国石油科技创新基金项目"陆上层间多次波压制技术"（2017D-5007-0302）、国家重点研发计划专项"基于压缩感知的海量数据高效并行处理"（2017YFB0202900）和中国石油集团"弹性波地震成像技术合作研发项目"联合资助


详细信息

作者简介: 蒋康康, 男, 1992年出生, 硕士, 主要从事地震信号处理方面的研究.E-mail:934311850@qq.com

通讯作者: 曹思远, 男, 1962年出生, 教授, 主要从事地震数据高分辨率处理方面的研究.E-mail:csy@cup.edu.cn

中图分类号: P631

收稿日期:2018-10-16
修回日期:2019-08-23
上线日期:2019-12-05

Seismic random noise attenuation based on Hessian matrix

JIANG KangKang^1,,
CAO SiYuan^2,,,
SUN XiaoMing²,
WANG Hang²,
WANG Hao²
1. China Railway First Survey And Design Institute Group Co., Ltd. Shaanxi Xi'an 710043, China
2. State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum-Beijing, Beijing 102249, China


More Information

Corresponding author: CAO SiYuan,E-mail:csy@cup.edu.cn

MSC: P631

--> Received Date: 16 October 2018
Revised Date: 23 August 2019
Available Online: 05 December 2019


摘要
摘要:随机噪声的压制在提高地震资料信噪比方面发挥重要作用.考虑到传统去噪方法在构造复杂地区难以取得理想的去噪结果，本文提出基于Hessian矩阵特征值对应的线性目标关系在多个尺度上对随机噪声进行压制.该方法将地震信号看作不同尺度的曲线，从而利用Hessian矩阵在曲线检测方面表现出的良好性能实现信噪分离.该方法与传统方法相比不受地层倾角的限制，因此能够处理构造较为复杂地区的地震数据.利用模型及实际资料对该方法进行了验证并与传统方法F-X反褶积的去噪结果做对比，结果表明基于Hessian矩阵的随机噪声压制方法在构造复杂地区能够保持有效信号的完整性.
关键词: 随机噪声/
多尺度/
Hessian矩阵/
保真度

Abstract:Random noise attenuation plays an important role in increasing signal-noise ratio. Traditional methods are not effective to attenuate the random noise in complex structural seismic data because part of the effective signal could be removed due to different strata dips. We proposed a new method to suppress the random noise on multiple scales based on the relationship between eigenvalues of the Hessian matrix and linear targets. This algorithm is to treat the effective signal as a curve and then apply curve detection to preserve effective signal. Compared with traditional methods,this method is not limited to the dip of the formation. As a result,it can suppress noise and protect effective signal when processing seismic data in complex structural area. The model and field data test could prove this method's effectiveness in suppressing random noise of the seismic data from complex structural area.
Key words:Random noise/
Multi-scale/
Hessian matrix/
Fidelity



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