Augmented geophysical data interpretation through automated velocity picking in semblance velocity images
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Velocity picking is the problem of picking velocity–time pairs based on a coherence metric between multiple seismic signals. Coherence as a function of velocity and time can be expressed as a 2D color semblance velocity image. Currently, humans pick velocities by looking at the semblance velocity image; this process can take days or even weeks to complete for a seismic survey. The problem can be posed as a geometric feature-matching problem. A feature extraction algorithm can recognize islands (peaks) of maximum semblance in the semblance velocity image: a heuristic combinatorial matching process can then be used to find a subset of peaks that maximizes the coherence metric. The peaks define a polyline through the image, and coherence is measured in terms of the summed velocity under the polyline and the smoothness of the polyline. Our best algorithm includes a constraint favoring solutions near the median solution for the local area under consideration. First, each image is processed independently. Then, a second pass of optimization includes proximity to the median as an additional optimization criterion. Our results are similar to those produced by human experts.
KeywordsCoherence Feature Extraction Optimization Criterion Geophysical Data Human Expert
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