Improving post-stack seismic data resolution based on Shearlet transform
GUO Aihua1,2, LU Pengfei1,3, YU Bo4, LU Chenghui4, WANG Bin5, WAN Lingna5
1. School of Information Engineering, East China University of Technology, Nanchang, Jiangxi 330013, China; 2. Jiangxi Engineering Technology Research Center of Nuclear Geoscience Data Science and System, East China University of Technology, Nanchang, Jiangxi 330013, China; 3. Jiangxi Engineering Laboratory on Radioactive Geoscience and Big Data Technology, East China University of Technology, Nanchang, Jiangxi 330013, China; 4. Baikouquan Oil Production Plant of Petrochina Xinjiang Oilfield Branch, Karamay, Xinjiang 834000, China; 5. School of Software, East China University of Technology, Nanchang, Jiangxi 330013, China
Abstract:Deconvolution, Q-compensation, spectral whitening, wavelet transform, and other methods often enlarge noise and reduce the signal-to-noise ratio of seismic data while improving the resolution of seismic data. Since seismic random noise obeys Gaussian distribution and has no directivity, effective signals and random noise can be separated in the Shearlet domain. The seismic signal is transformed into the Shearlet domain by Shearlet transform. The coefficients in the Shearlet domain are compensated reasonably, and then the inverse Shearlet transform is carried out, which can improve the resolution of seismic data. Combined with the two characteristics of Shearlet transform, firstly, the random noise coefficients in the Shearlet domain are discarded, and at the same time, only the coefficients in the Shearlet domain within the dominant frequency band are compensated, and the frequency is raised. This not only improves the resolution of seismic data but also maintains the signal-to-noise ratio of seismic data. The proces-sing results of synthetic seismic data and post-stack real data show that this method can improve the resolution of post-stack seismic data.
赵玉敏.信噪比约束下的提高分辨率方法研究[D]. 北京:中国石油大学(北京), 2017.ZHAO Yumin.Research on Resolution Enhancement Method with Adaptive Lateral Constraint Based on Denoising Filter[D]. China University of Petroleum(Beijing), Beijing, 2017.
[2]
李红彩, 罗军梅.提高分辨率处理技术在墩塘地区的应用[J]. 工艺技术, 2019, 9:227-228.LI Hongcai, LUO Junmei.Application of processing technology to improve resolution in Duntang area[J]. Process technology, 2019, 9:227-228.
[3]
魏忠宇.径向道域反褶积提高地震资料分辨率方法研究[D]. 吉林长春:吉林大学, 2020.WEI Zongyu.The Radial Trace Domain Deconvolution to Improve the Resolution of Seismic Data[D]. Jinlin University, Changchun, Jilin, 2020.
[4]
余连勇, 胡光义, 赵岩, 等.稳定的反Q滤波统一算法及其在地震资料高分辨率处理中的应用[J]. 中国海上油气, 2014, 26(4):29-33.YU Lianyong, HU Guangyi, ZHAO Yan, et al.A unified algorithm of stable inverse Q filtering and its application to high resolution processing of seismic data[J]. China Offshore Oil and Gas, 2014, 26(4):29-33.
[5]
董相杰, 余杰, 王珊, 等.强衰减地层VSP反Q滤波方法[J]. 石油地球物理勘探, 2014, 49(5):871-876.DONG Xiangjie, YU Jie, WANG Shan, et al.VSP inverse Q filtering in strong attenuation formation[J]. Oil Geophysical Prospecting, 2014, 49(5):871-876.
[6]
张固澜, 贺振华, 王熙明, 等.地震波频散效应与反Q滤波相位补偿[J]. 地球物理学报, 2014, 57(5):1655-1663.ZHANG Gulan, HE Zhenhua, WANG Ximing, et al.Seismic wave dispersion effects and inverse Q filter phase compensation[J]. Chinese Journal of Geophy-sics, 2014, 57(5):1655-1663.
[7]
张固澜, 林进, 王熙明, 等.一种自适应增益限的反Q滤波[J]. 地球物理学报, 2015, 58(7):2525-2535.ZHANG Gulan, LIN Jin, WANG Ximing, et al.A self-adaptive approach for inverse Q filtering[J]. Chinese Journal of Geophysics, 2015, 58(7):2525-2535.
[8]
程志国, 娄兵, 姚茂敏, 等.VSP井控Q值提取和补偿方法在玛湖地区的应用[J]. 物探化探计算技术, 2015, 37(6):749-753.CHENG Zhiguo, LOU Bing, YAO Maomin, et al.Application of VSP well controlled Q extraction and compensation method in Mahu area[J]. Computing Techniques for Geophysical and Geochemical Exploration, 2015, 37(6):749-753.
[9]
陈树民, 刘礼农, 张剑锋, 等.一种补偿介质吸收叠前时间偏移技术[J]. 石油物探, 2018, 57(4):576-583.CHEN Shumin, LIU Linong, ZHANG Jianfeng, et al.A deabsorption prestack time migration technology[J]. Geophysical Prospecting for Petroleum, 2018, 57(4):576-583.
[10]
孙明, 赵宝银, 陈伟超, 等.针对冀东南堡4号中浅层河道砂体识别的提高分辨率处理[J]. 物探化探计算技术, 2018, 40(4):417-424.SUN Ming, ZHAO Baoyin, CHEN Weichao, et al.High resolution seismic data processing aimed at channel sand body recognition in middle shallow layers, Jidong Nanpu 4 structure[J]. Computing Techniques for Geophysical and Geochemical Exploration, 2018, 40(4):417-424.
[11]
赵秋芳, 党鹏飞, 云美厚, 等.不同时频变换谱比法品质因子Q估算效果对比分析[J]. 地球物理学进展, 2018, 33(5):2097-2101.ZHAO Qiufang, DANG Pengfei, YUN Meihou, et al.Comparative analysis of quality factor Q estimated by spectral ratio method based on different time frequency transform[J]. Progress in Geophysics, 2018, 33(5):2097-2101.
[12]
周衍, 饶莹.地震反Q滤波应用于碳酸盐岩储层特征描述[J]. 地球物理学报, 2018, 61(1):284-292.ZHOU Yan, RAO Ying.Seismic inverse Q filtering for carbonate reservoir characterization[J]. Chinese Journal of Geophysics, 2018, 61(1):284-292.
[13]
王季.基于Hilbert谱白化的高分辨率地震资料处理[J]. 煤炭学报, 2012, 37(1):50-54.WANG Ji.High resolution seismic processing based on whitening of Hilbert spectrum[J]. Journal of China Coal Society, 2012, 37(1):50-54.
[14]
颜中辉, 方刚, 徐华宁, 等.希尔伯特谱白化方法在海洋地震资料高分辨率处理中的应用[J]. 海洋地质与第四纪地质, 2018, 38(4):212-220.YAN Zhonghui, FANG Gang, XU Huaning, et al.The application of Hilbert spectral whitening method to high resolution processing of marine seismic data[J]. Marine Geology & Quaternary Geology, 2018, 38(4):212-220.
[15]
路鹏飞, 郭爱华, 赵宝银, 等.利用小波分析技术提高老爷庙油田地震资料分辨率[J]. 石油地球物理勘探, 2012, 47(2):272-276.LU Pengfei, GUO Aihua, ZHAO Baoyin, et al.Seismic data resolution improvement in Laoyemiao by wavelet analysis[J]. Oil Geophysical Prospecting, 2012, 47(2):272-276.
[16]
黄捍东, 冯娜, 王彦超, 等.广义S变换地震高分辨率处理方法研究[J]. 石油地球物理勘探, 2014, 49(1):82-88.HUANG Handong, FENG Na, WANG Yanchao, et al.High resolution seismic processing based on gene-ralized S transform[J]. Oil Geophysical Prospecting, 2014, 49(1):82-88.
[17]
杨子鹏, 宋维琪, 刘军, 等.联合广义S变换和压缩感知提高地震资料分辨率[J/OL]. 地球物理学进展(网络首发), 2020:1-11.YANG Zipeng, SONG Weiqi, LIU Jun, et al.Combine generalized S transform with compressed sensing to improve the resolution of seismic data[J/OL]. Progress in Geophysics, 2020:1-11.
[18]
宋鑫磊.地震资料反Q滤波法研究及应用[D]. 四川成都:成都理工大学, 2019.SONG Xinlei.The Study and Application of Inverse Q-filtering for Seismic Data[D]. Chengdu University of Technology, Chengdu, Sichuan, 2019.
[19]
李曙光, 唐建明, 徐天吉, 等.几种提高地震资料分辨率的方法及效果分析[J]. 勘探地球物理进展, 2010, 33(5):323-327.LI Shuguang, TANG Jianming, XU Tianji, et al.Methods for improving seismic data resolution[J]. Progress in Exploration Geophysics, 2010, 33(5):323-327.
[20]
王常波.基于Shearlet稀疏变换基的压缩感知重建技术[J]. 地球物理学进展, 2018, 33(6):2441-2449.WANG Changbo.Compressed sensing seismic data reconstruction with Shearlet transformation[J]. Progress in Geophysics, 2018, 33(6):2441-2449.
[21]
王德营, 李振春, 董烈乾.Shearlet域和TT域联合压制面波方法[J]. 石油地球物理勘探, 2014, 49(1):53-60.WANG Deying, LI Zhenchun, DONG Lieqian.Surface wave joint suppression based on Shearlet transformation and time-time transformation[J]. Oil Geophysical Prospecting, 2014, 49(1):53-60.
[22]
李民, 周亚同, 李梦瑶, 等.Shearlet域基于非局部均值的地震信号去噪[J/OL]. 重庆大学学报(网络首发), 2019.LI Min, ZHOU Yatong, LI Mengyao, et al.Denoising of seismic signals based on non-local mean in Shearlet domain[J/OL]. Journal of Chongqing University, 2019.
[23]
童思友, 高航, 刘锐, 等.基于Shearlet变换的自适应地震资料随机噪声压制[J]. 石油地球物理勘探, 2019, 54(4):744-750.TONG Siyou, GAO Hang, LIU Rui, et al.Seismic random noise adaptive suppression based on the Shearlet transform[J]. Oil Geophysical Prospecting, 2019, 54(4):744-750.
[24]
董新桐, 马海涛, 李月.丘陵地带地震资料随机噪声压制新技术:高阶加权阈值函数的Shearlet变换[J]. 地球物理学报, 2019, 62(10):4039-4046.DONG Xintong, MA Haitao, LI Yue.The new technology for suppression of hilly land seismic random noise:Shearlet transform and the high order weighted threshold function[J]. Chinese Journal of Geophysics, 2019, 62(10):4039-4046.
[25]
程浩, 王德利, 王恩德, 等.尺度自适应三维Shearlet变换地震随机噪声压制[J]. 石油地球物理勘探, 2019, 54(5):970-978.CHENG Hao, WANG Deli, WANG Ende, et al.Seismic random noise suppression based on scale adaptive 3D Shearlet transform[J]. Oil Geophysical Prospecting, 2019, 54(5):970-978.
[26]
薛林, 程浩, 巩恩普, 等.Shearlet域自适应阈值地震数据随机噪声压制[J]. 石油地球物理勘探, 2020, 55(2):282-291.XUE Lin, CHENG Hao, GONG Enpu, et al.Random noise suppression using adaptive threshold in Shearlet domain[J]. Oil Geophysical Prospecting, 2020, 55(2):282-291.
[27]
Karbalaali H, Javaherian A, Dahlke S, et al.Seismic channel edge detection using 3D Shearlets-a study on synthetic and real channelized 3D seismic data[J]. Geophysical Prospecting, 2018, 66(7):1272-1289.