Automatic Fault Surface Detection Using 3D Hough Transform

Transcription

1 Automatic Fault Surface Detection Using 3D Hough Transform Zhen Wang and Ghassan AlRegib Center for Energy and Geo Processing - CeGP School of Electrical and Computer Engineering Georgia Institute of Technology, Atlanta, GA, U.S.A. {zwang313, alregib}@gatech.edu

2 Outline Motivation and Dataset Proposed Method Fault Points Highlighting 3D Hough Transform Fault Surface Labeling Conclusion 2/25

3 Motivation Manual interpretation is time consuming and labor intensive Two main drawbacks of fault detection in 2D sections: 1. Local parameters need to be tweaked in each section 2. Ignore coherency between neighboring sections Available fault surface detection methods in 3D seismic datasets commonly involve many parameters 3/25

4 Dataset A subset of Netherlands offshore F3 block Dimension: Crossline*Inline*Time = 251*226*176 Resolution: inline & crossline: 50m, time: 4ms Z (time) X (crossline) Seismic Image s( x, z) 4/25

5 Outline Motivations and Dataset Proposed Method Fault Points Highlighting 3D Hough Transform Fault Surface Labeling Conclusion 5/25

6 Diagram of Fault Surface Detection Seismic Volumes Fault Points Highlighting 3D Hough Transform Fault Surfaces Labeling Fault Surfaces 6/25

7 Discontinuity Attribute Discontinuity attribute derived from the semblance attribute proposed by Marfurt et al. [1] z round tan i tan j ln( ) x y increases contrast between faults and horizons rd indicates the size of analysis cubes [1]. K. J. Marfurt, V. Sudhaker, A. Gersztenkorn, K. D. Crawford, and S. E. Nissen, Coherency calculations in the presence of structural dip, Geophysics, vol. 64, no. 1, pp , /25

8 Fault Points Highlighting 1 D[ x, y, z] D B[ x, y, z] 0 Otherwise 0 8/25

9 Diagram of Fault Surface Detection Seismic Volumes Fault Points Highlighting 3D Hough Transform Fault Surfaces Labeling Fault Surfaces 9/25

10 2D Hough Transform Mapping from image space to parameter space Image Space Image Space Parameter Space Parameter Space y r x cos y sin r 0 0 x, y 0 0 x cos y sin r r 0, 0 r 0 0 x 10/25

11 2D Hough Transform Mapping from image space to parameter space Image Space Parameter Space y r 0 x, y 0 0 x cos y sin r x1, y1 r x cos y sin r 0 0 x cos y sin r x 11/25

12 Parameterized Equation in the Spherical Coordinates System A normal vector and a point define a plane n sin cos,sin sin,cos r 0 ( x0, y0, z0) In the data space: sin cos x sin sin y cos z nr sin cos x sin sin y cos z Distance from origin to the determined plane 12/25

13 3D Hough Transform z sin cos x sin sin y cos z y x 13/25

14 3D Hough Transform z y x 14/25

15 3D Hough Transform z y x 15/25

16 Accumulators in Parameter Space Each voxel is an accumulator that records the number of intersected curves 3D Hough Transform 16/25

17 Detected Fault Planes Fault planes are detected by selecting accumulators with the largest values False planes are removed based on the user-defined constraints of positions and directions 17/25

18 Diagram of Fault Surface Detection Seismic Volumes Fault Points Highlighting 3D Hough Transform Fault Surfaces Labeling Fault Surfaces 18/25

19 Weighted fitting of fault planes Divide z coordinates into M intervals: [( i 1)* r 1, i* r], i 1,2,, M Apply weighted fitting on points in each depth interval c c, c, c A x, z, 1 B y i i i T WAc WB : the coefficients of fitted surface : x and z coordinates of points : y coordinates of points W diag D x, y, z i i i : weights are discontinuity values 19/25

20 Coefficients Estimation Estimate coefficients by minimizing the estimation error: 2 norm of the cˆ arg min WB W Ac Analytic solution of : c ĉ 2 2 T T T T ˆ 1 c A W WA A W WB 20/25

21 Comparison Detected Results Ground Truth 21/25

22 Outline Motivations and Dataset Proposed Method Fault Points Highlighting 3D Hough Transform Fault Surface Labeling Conclusion 22/25

23 Conclusion 3D Hough transform can be applied to detect fault surfaces Fewer parameters involved reduced the labor and time cost of interpreters Our future work will focus on the automatic calculation of parameters and the improvement of accuracy 23/25

24 Related Work Z. Wang and G. AlRegib, Fault detection in 3D seismic data using the Hough transform and tracking vectors, submitted to IEEE Transactions on Geoscience and Remote Sensing. Z. Wang, D. Temel and G. AlRegib, "Fault detection using color blending and color transformations," to be presented at 2014 IEEE GlobalSIP, Atlanta, Georgia, Dec. 3-5, Z. Wang, Z. Long, G. AlRegib, A. Asjad, and M. A. Deriche, "Automatic fault tracking across seismic volumes via tracking vectors," to be presented at IEEE International Conference on Image Processing (ICIP), Paris, France, Oct , Z. Wang and G. AlRegib, "Fault detection in seismic datasets using Hough transform," Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP), Florence, Italy, May /25

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