3D modeling of geological anomalies based on segmentation of multiattribute fusion
Liu Zhi-Ning1, Song Cheng-Yun1, Li Zhi-Yong1, Cai Han-Peng2, Yao Xing-Miao2, and Hu Guang-Min1
1. School of Communication and Information Engineering, University of Electronic and Technology of China, Chengdu 611731, China.
2. School of Resources and Environment, University of Electronic and Technology of China, Chengdu 611731, China.
Abstract：
3D modeling of geological bodies based on 3D seismic data is used to define the shape and volume of the bodies, which then can be directly applied to reservoir prediction, reserve estimation, and exploration. However, multiattributes are not effectively used in 3D modeling. To solve this problem, we propose a novel method for building of 3D model of geological anomalies based on the segmentation of multiattribute fusion. First, we divide the seismic attributes into edge- and region-based seismic attributes. Then, the segmentation model incorporating the edge- and region-based models is constructed within the levelset-based framework. Finally, the marching cubes algorithm is adopted to extract the zero level set based on the segmentation results and build the 3D model of the geological anomaly.Combining the edge-and region-based attributes to build the segmentation model, we satisfy the independence requirement and avoid the problem of insufficient data of single seismic attribute in capturing the boundaries of geological anomalies. We apply the proposed method to seismic data from the Sichuan Basin in southwestern China and obtain 3D models of caves and channels. Compared with 3D models obtained based on single seismic attributes, the results are better agreement with reality.
. 3D modeling of geological anomalies based on segmentation of multiattribute fusion[J]. APPLIED GEOPHYSICS, 2016, 13(3): 519-528.
[1]
Barnes, A. E., 2000, Attributes for automated seismic facies analysis: 70th Annual International Meeting, SEG, Expanded Abstracts, 553-556.
[2]
Caumon, G., Collon-Drouaillet, P., De Veslud, C. L. C., and Sausse, J., 2009, Surface-based 3D modeling of geological structures: Mathematical Geosciences, 41(8), 927−945.
[3]
Chan, T. F., and Vese, L., 2001, Active contours without edges: Image processing, IEEE transactions on, 10(2), 266−277.
[4]
Chopra, S., and Marfurt, K. J., 2005, Seismic attributes—A historical perspective: Geophysics, 70(5), 3−28.
[5]
Frank, T., Tertois, A. L., and Mallet, J. L., 2007, 3D-reconstruction of complex geological interfaces from irregularly distributed and noisy point data: Computers & Geosciences, 33(7), 932−943.
[6]
Gao, D., 2003, Volume texture extraction for 3D seismic visualization and interpretation: Geophysics, 68(4), 1294−1302.
[7]
Hale, D., and Emanuel, J., 2003, Seismic interpretation using global image segmentation: 73rd Annual International Meeting, SEG, Expanded Abstracts, 2410−2413.
[8]
Hale, D., 2013, Methods to compute fault images, extract fault surfaces, and estimate fault throws from 3D seismic images: Geophysics, 78(2), 33−43.
[9]
Halpert, A. D., Clapp, R. G., and Biondi, B., 2009, Seismic image segmentation with multiple attributes: 79th Annual International Meeting, SEG, Expanded Abstracts, 3700−3704.
[10]
Halpert, A. D., Clapp, R. G., and Biondi, B., 2014, Salt delineation via interpreter-guided 3D seismic image segmentation: Interpretation, 2(2), 79−88.
[11]
Hanning, T., and Pisinger, G., 2002, A pixel-based segmentation algorithm of color images by N-level-fitting: 5th IASTED International Conference on Computer Graphics and Imaging (Honolulu, Hawaii), 138−144.
[12]
Kadlec, B. J., Dorn, G. A., and Tufo, H. M., 2008, Confidence and curvature-guided level sets for channel segmentation: 78th Annual International Meeting, SEG, Expanded Abstracts, 879−883.
[13]
Kadlec, B. J., 2009, Interactive GPU-based visualization and structure analysis of three-dimensional implicit surfaces for seismic interpretation: PhD Thesis, University of Colorado, Colorado.
[14]
Li, C., Xu, C., Gui, C., and Fox, M. D., 2010, Distance regularized level set evolution and its application to image segmentation: Image Processing, IEEE Transactions on, 19(12), 3243−3254.
[15]
Lomask, J., Biondi, B., and Shragge, J., 2004, Image segmentation for tracking salt boundaries: 74th Annual International Meeting, SEG, Expanded Abstracts, 2443-2446.
[16]
Lomask, J., Clapp, R. G., and Biondi, B., 2006, Parallel implementation of image segmentation for tracking 3D salt boundaries: 68th Annual Conference and Exhibition, EAGE, Extended Abstracts, 1402−1405.
[17]
Lomask, J., Clapp, R. G., and Biondi, B., 2007, Application of image segmentation to tracking 3D salt boundaries: Geophysics, 72(4), 47−56.
[18]
Lorensen, W. E., and Cline, H. E., 1987, Marching cubes: A high resolution 3D surface construction algorithm:ACM siggraph computer graphics, 21(4), 163-169.
[19]
Love, P. L., and Simaan, M., 1985, Segmentation of a seismic section using image processing and artificial intelligence techniques: Pattern Recognition, 18(6), 409−419.
[20]
Osher, S., and Sethian, J. A., 1988, Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations: Journal of computational physics, 79(1), 12−49.
[21]
Wen, X., He, Z., Huang, D., and Chen, X., 2011, Highlighting display of geologic bodies based on directivity filtering: Applied Geophysics, 8(4), 355−362.
[22]
Weng, Z. P., 2013, Fast Building and Refreshing Three-dimensional Models of Geological Entity: PhD Thesis, China University of Geosciences, Wuhan.
[23]
Zhao, H. K., Osher, S., and Fedkiw, R., 2001, Fast surface reconstruction using the level set method: Variational and Level Set Methods in Computer Vision, Proceedings, IEEE Workshop on, 194−201.
[24]
Zhang J., Liu, Z., Zhu B, Feng, D., Zhang, M., and Zhang, X., 2011, Fluvial reservoir characterization and identification: A case study from Laohekou Oilfield: Applied Geophysics, 8(3), 181−188.
2016, Vol. 13 
