Augmented geophysical data interpretation through automated velocity picking in semblance velocity images

Abstract. 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.