A practical data-driven optimization strategy for Gaussian beam migration

Abstract

Gaussian beam migration (GBM) is an efficient and accurate depth imaging technique, which allows us to resolve steep-dip structures and image multiple arrivals. Similar to Kirchhoff migration, GBM projects reflection events into the subsurface along traveltime isochrons. For data with low folds or signal-to-noise ratio (S/N), it produces migration artifacts, making it difficult for subsequent interpretation and attribute analysis. We have developed a data-driven optimization strategy to solve this problem. First, at the source and beam center locations, we estimate the instantaneous emergence angles of specular rays using semblance analysis for local common-shot and common-receiver gathers. Then, a quality control factor is designed to enhance the imaging results of coherent signals around the specular rays. Synthetic and field data examples demonstrate that our optimization strategy enables us to improve the imaging quality of GBM, especially for sparsely acquired and low-S/N data.