Methods to compute salt likelihoods and extract salt boundaries from 3D seismic images

Salt body interpretation is important for building subsurface models and interpreting seismic horizons and faults which might be truncated by the salt. Salt interpretation often includes two steps: highlighting salt boundaries with a salt attribute image, and extracting salt boundaries from the attribute image. Although both steps have been automated to some extent, salt interpretation today typically still requires significant manual e↵ort. From a 3D seismic image, I first e ciently compute a salt likelihood image, in which the ridges of likelihood values indicate locations of salt boundaries. I then extract salt samples on the ridges. These samples can be directly connected to construct salt boundaries in cases when salt structures are simple and the boundaries are clean. In more complicated cases, these samples may be noisy and incomplete, and some samples are outliers unrelated to salt boundaries. Therefore, I finally develop a method to reasonably fit noisy salt samples, fill gaps, and handle outliers to simultaneously construct multiple salt boundaries. In this step of constructing salt boundaries, I also propose a convenient way to incorporate human interactions to obtain more accurate salt boundaries in especially complicated cases. I demonstrate the methods of computing salt likelihoods and constructing salt surfaces by using a 3D seismic image containing multiple salt bodies.