Common‐offset common‐reflection‐surface attributes estimation with differential evolution

Abstract

ABSTRACTCommon‐reflection surface is a method to describe the shape of seismic events, typically the slopes (dip) and curvature portions (traveltime). The most systematic approach to estimate the common‐reflection surface traveltime attributes is to employ a sequence of single‐variable search procedures, inheriting the advantage of a low computational cost, but also the disadvantage of a poor estimation quality. A search strategy where the common‐reflection surface attributes are globally estimated in a single stage may yield more accurate estimates. In this paper, we propose to use the bio‐inspired global optimization algorithm differential evolution to estimate all the two‐dimensional common‐offset common‐reflection surface attributes simultaneously. The differential evolution algorithm can provide accurate estimates for the common‐reflection surface traveltime attributes, with the benefit of having a small set of input parameters to be configured. We apply the differential evolution algorithm to estimate the two‐dimensional common‐reflection surface attributes in the synthetic Marmousi data set, contaminated by noise, and in a land field data with a small fold. By analysing the stacked and coherence sections, we could see that the differential evolution based common‐offset common‐reflection surface approach presented significant signal‐to‐noise ratio enhancement.