摘要
东海丽水区地质结构复杂,受浅层火成岩地震信号屏蔽影响,下覆地层反射能量弱,信噪比低。传统噪音衰减技术在该区存在噪音衰减不彻底及高陡倾角信号损伤等问题。为了解决以上问题,结合工区资料特征开发了频率切片域局部窗口两步法组合噪音衰减技术,该方法首先采用Cadzow滤波技术进行低信噪比资料的强能量噪音的去除,然后采用Eigenimage滤波技术进行弱能量噪音的进一步去除,频率切片域局部窗口两步法噪音衰减技术,完成了东海丽水区火成岩下覆低信噪比区的提高信噪比处理,改善了资料品质,落实了目标区烃源厚度。结果表明,该方法在复杂构造区信噪分离能力强,有效信号保持度高,能够提升复杂构造区地震资料的信噪比。
Because of the complex geological structure of Lishui area in the East China Sea and its liability to be affected by seismic signal shielding of shallow igneous rocks,the underlying strata tend to have weak reflection energy and low signal-to-noise ratio.Traditional noise attenuation techniques in this area are usually troubled by such problems as incomplete noise attenuation and high dip signal damage.In order to solve these problems,based on the seismic characteristics of the working area,a two-step combined noise attenuation technique of local window in frequency slice domain is developed.Firstly,the Cadzow filtering technique is used to remove the strong energy noise of the low SNR data.The noise attenuation effect of Cadzow and Eigenimage technologies are then tested.Finally,the two methods of Cadzow and Eigenimage are combined in the local window of the frequency slice domain to complete the processing of improving the signal-to-noise ratio in the complex survey.The results show that this method has strong signal noise separation ability and high effective signal retention in complex structural area,which significantly improves the signal to noise ratio of target data and supports the exploration and research of underlying targets of igneous rocks in the target area.
作者
常坤
孙雷鸣
王新领
陈雅丽
曾维辉
姜占东
Chang Kun;Sun Leiming;Wang Xinling;Chen Yali;Zeng Weihui;Jiang Zhandong(Data Processing Company,Geophysical-COSL,Zhanjiang Guangdong 524057,China;Zhanjiang Branch Company,China National Offshore Oil Corporation,Zhanjiang Guangdong 524057,China)
出处
《工程地球物理学报》
2022年第6期886-892,共7页
Chinese Journal of Engineering Geophysics
基金
中海油服物探事业部科研项目(编号:WTB21YF010)。
关键词
复杂构造
低信噪比
特征值分解
信噪分离
complex structure
low signal-to-noise ratio
eigenvalue decomposition
signal noise separation