Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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In this paper, we examine the applicability and repeatability of a genetic algorithm to automatically correlate horizons across faults in seismic data images. This problem arises from geological sciences where it is a subtask of structural interpretation of those images which has not been automated before. Because of the small amount of local information contained in seismic images, we developed a geological model in order to reduce interpretation uncertainties. The key problem is an optimisation task which cannot be solved exhaustively since it would cause exponential computational cost. Among stochastic methods, a genetic algorithm has been chosen to solve the application problem. Repeated application of the algorithm to four different faults delivered an acceptable solution in 94-100% of the experiments. The global optimum was equal to the geologically most plausible solution in three of the four cases.