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