Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
Point Processes for Unsupervised Line Network Extraction in Remote Sensing
IEEE Transactions on Pattern Analysis and Machine Intelligence
A genetic algorithm for automated horizon correlation across faults in seismic images
IEEE Transactions on Evolutionary Computation
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Oil and gas exploration decisions are made based on inferences obtained from seismic data interpretation. While 3-d seismic data become widespread and the data-sets get larger, the demand for automation to speed up the seismic interpretation process is increasing as well. Image processing tools such as auto-trackers assist manual interpretation of horizons, seismic events representing boundaries between rock layers. Auto-trackers works to the extent of observed data continuity; they fail to track horizons in areas of discontinuities such as faults. In this paper, we present a method for automatic horizon matching across faults based on a Bayesian approach. A stochastic matching model which integrates 3-d spatial information of seismic data and prior geological knowledge is introduced. A multi-resolution simulated annealing with reversible jump Markov Chain Monte Carlo algorithm is employed to sample from a-posteriori distribution. The multi-resolution is defined in a scale-space like representation using perceptual resolution of the scene. The model was applied to real 3-d seismic data, and has shown to produce horizons matchings which compare well with manually obtained matching references.