DocumentCode :
415573
Title :
Automatic method for correlating horizons across faults in 3D seismic data
Author :
Admasu, Fitsum ; Toennies, Klaus
Author_Institution :
Comput. Vision Group, Univ. of Magdeburg, Germany
Volume :
1
fYear :
2004
fDate :
27 June-2 July 2004
Abstract :
Horizons are visible boundaries between certain sediment layers in seismic data, and a fault is a crack of horizons and it is recognized in seismic data by the discontinuities of horizons layers. Interpretation of seismic data is a time-consuming manual task which is only partially supported by computer methods. In this paper, we present an automatic method for horizon correlation across faults in 3D seismic data. As automating horizons correlations using only seismic data features is not feasible, we reformulated the correlation task as a non-rigid continuous point matching problem. Seismic features on both sides of the fault are gathered and an optimal match is found based on geological fault displacement model. One side of the fault is the floating image while the other side is the reference image. First, very prominent regions on both sides are automatically extracted and a match between them is found. Sparse fault displacements are then computed for these regions and they are used to calculate parameters for the fault displacement model. A multi-resolution simulated annealing optimization scheme is then used for the continuous point matching. The method was applied to real 3D seismic data, and has shown to produce geologically acceptable horizons correlations.
Keywords :
correlation methods; geophysical signal processing; image matching; image resolution; seismology; simulated annealing; 3D seismic data; automatic horizon correlation method; computer methods; data interpretation; fault displacements; floating image; geological fault displacement model; multiresolution simulated annealing; nonrigid continuous point matching; optimization; reference image; sediment layers; Acoustic reflection; Computer vision; Geologic measurements; Geology; Image analysis; Image segmentation; Optimal matching; Sediments; Seismic measurements; Seismic waves;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on
ISSN :
1063-6919
Print_ISBN :
0-7695-2158-4
Type :
conf
DOI :
10.1109/CVPR.2004.1315021
Filename :
1315021
Link To Document :
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