Volumetric edge detection of seismic data based on arbitrarily rotated windowed Hilbert transform
XU He1, CHEN Xuehua1,2, LYU Bingnan2, LI Kangyi2, XU Bin2
1. State Key Laboratory of Oil & Gas Reservoir Geology and Exploitation, Chengdu University of Technology, Chengdu, Sichuan 610059, China; 2. Key Lab of Earth Exploration & Information Techniques of Ministry of Education, Chengdu University of Technology, Chengdu, Sichuan 610059, China
Abstract:The edge detection algorithm based on Hilbert transform usually extracts comprehensive fracture information from real seismic data. The information is very complicated and may weaken some effective information, making difficult to distinguish data characteristics in a particular direction, and unconducive to analyzing fracture orientation. This paper proposes a volumetric edge detection algorithm based on fracture directionality. It uses a Gaussian window to process the Hilbert operator, and then rotates the operator counterclockwise at any angle on a direction detecting template. An arbitrarily rotated windowed Hilbert transform (ARWHT) edge detection operator is constructed. It can extract effective edges in any specific direction. The algorithm is used for volumetric edge detection of 3D seismic data. It can not only extract discontinuous features in any direction of seismic data, but also highlight local abnormal information and reduce the influence of noises, so as to obtain a more accurate trend and distribution of reservoir fractures. Applications on model and raw data have proved that the algorithm has the following characteristics. For large faults with known strike or fracture systems with similar orientation, in order to strengthen the discontinuous information of the strike or orientation, the operator perpendicular to the strike or orientation should be selected. For complex fracture systems containing information in various directions, more angles should be selected to extract edge information for fusion, so as to display more effective information. This is conducive to fine seismic interpretation.
陈学华,贺振华,文晓涛,等.基于广义S变换的裂缝分频边缘检测方法[J].吉林大学学报(地球科学版),2011,41(5):1605-1609.CHEN Xuehua,HE Zhenhua,WEN Xiaotao,et al.Fracture multi-frequency edge detection based on ge-neralized S transform[J].Journal of Jilin University(Earth Science Edition),2011,41(5):1605-1609.
[2]
Wu X M,Liang L M,Shi Y Z,et al.FaultSeg3D:Using synthetic data sets to train an end-to-end convolutional neural network for 3D seismic fault segmentation[J].Geophysics,2019,84(3):IM35-IM45.
[3]
Wu X M,Liang L M,Shi Y Z,et al.Multitask lear-ning for local seismic image processing:fault detection, structure-oriented smoothing with edge-preserving, and seismic normal estimation by using a single con-volutional neural network[J].Geophysical Journal International,2019,219(3):2097-2109.
[4]
Wu X M,Shi Y Z,Fomel S,et al.FaultNet3D:Predicting fault probabilities,strikes,and dips with a single convolutional neural network[J].IEEE Transactions on Geoscience and Remote Sensing,2019,57(11):9138-9155.
[5]
丁燕,杜启振,Qamar Yasin,等.基于深度学习的裂缝预测在S区潜山碳酸盐岩储层中的应用[J].石油物探,2020,59(2):267-275.DING Yan,DU Qizhen,Qamar Yasin,et al.Fracture prediction based on deep learning:Application to a buried hill carbonate reservoir in the S area[J].Geophysical Prospecting for Petroleum,2020,59(2):267-275.
[6]
党志敏,贺振华,黄德济.GHT在含噪信号边缘检测中的应用效果分析[J].石油工业计算机应用,2008,16(2):19-21.DANG Zhimin,HE Zhenhua,HUANG Deji.GHT application results analysis in noisy signal edge detection analysis[J].Oil Industry Computer Applications, 2008,16(2):19-21.
Luo Y, Al-Dossary S,Marhoon M,et al.Generalized Hilbert transform and its applications in geophysics[J].The Leading Edge,2003,22(3):198-202.
[9]
陈学华,贺振华,黄德济.地震资料的高阶伪希尔伯特变换边缘检测[J].地球物理学进展,2008,23(4):1106-1110.CHEN Xuehua,HE Zhenhua,HUANG Deji.Seismic data edge detection based on higher-order pseudo Hilbert transform[J].Progress in Geophysics,2008,23(4):1106-1110.
[10]
陈学华,贺振华,黄德济.高阶伪希尔伯特变换在边缘检测中的应用[J].数据采集与处理,2008,23(2):224-227.CHEN Xuehua,HE Zhenhua,HUANG Deji.High-order pseudo Hilbert transform and application in edge detection[J].Journal of Data Acquisition & Proce-ssing,2008,23(2):224-227.
李斌,陈学华,贺振华,等.基于多尺度加窗希尔伯特变换的地震资料体边缘检测[J].石油物探,2015,54(3):345-349.LI Bin,CHEN Xuehua,HE Zhenhua,et al.Seismic data 3D edge detection based on multi-scale windowed Hilbert transform[J].Geophysical Prospecting for Petroleum,2015,54(3):345-349.
[13]
Read R R,Treitel S.The stabilization of two-dimensional recursive filters via the discrete Hilbert transform[J].IEEE Transactions on Geoscience Electro-nics,1973,7(11):153-160.
[14]
Kohlmann K.Corner detection in natural images based on the 2-D Hilbert transform[J].Signal Processing, 1996,48(3):225-234.
[15]
王珂,肖鹏峰,冯学智,等.基于改进二维离散希尔伯特变换的图像边缘检测方法[J].测绘学报,2012,41(3):421-427.WANG Ke,XIAO Pengfeng,FENG Xuezhi,et al.The modified algorithm of image edge features detection based on 2-D discrete Hilbert transform[J].Acta Geodaetica et Cartographica Sinica,2012,41(3):421-427.
[16]
周连敏,何书梅,赵郁文,等.复合曲流河道内的单河道识别[J].石油地球物理勘探,2019,54(1):175-181.ZHOU Lianmin,HE Shumei,ZHAO Yuwen,et al.Single channel identification in a meandering river with compound channels[J].Oil Geophysical Prospecting,2019,54(1):175-181.
[17]
Liu C,Chen C L,Wang D.Seismic dip estimation based on the two-dimensional Hilbert transform and its application in random noise attenuation[J].Applied Geophysics, 2015,12(1):55-63.
[18]
Lyu B N,Chen X H,Li J,et al.An edge detection algorithm of 3D seismic data based on interval two-dimensional Hilbert transform[C].CPS/SEG Beijing 2018 International Geophysical Conference & Exposition, Electronic Papers,2018.
[19]
吕丙南,陈学华,徐赫,等.空间域加窗二维希尔伯特变换在三维地震资料[J].石油地球物理勘探, 2020,55(3):661-668.LYU Bingnan,CHEN Xuehua,XU He,et al.Application of spatial-windowed 2D Hilbert transform in vo-lumetric edge detection of 3D seismic data[J].Oil Geophysical Prospecting,2020,55(3):661-668.