Computer Vision, Graphics, and Image Processing
ACO algorithms for the quadratic assignment problem
New ideas in optimization
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Soft Computing - A Fusion of Foundations, Methodologies and Applications
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Data mining with an ant colony optimization algorithm
IEEE Transactions on Evolutionary Computation
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The mapping of the fault network is of key importance in reservoir characterization. Interpretation of faults in seismic data is today most commonly a manual task. This is time consuming and difficult to do accurately. Automatic extraction of the fault surfaces would allow user-interaction at a higher level. In this paper, we implemented and tested an automatic fault tracking scheme based on the ant colony algorithm. We applied the proposed method to auto-track the faults on seismic data. Its performance on real seismic data indicates that the proposed method can effectively reduce the noise level and improve the continuity of faults on seismic coherency cube. In order to assess the performance of the auto-tracking for faults, we make a comparison of the results from the auto-tracking with manual interpretations of fault geometry over the same area. The comparison demonstrates that the proposed method detects most faults accurately.