I propose a new approach to automate salt body interpretation, which is important for seismic interpretation and subsurface modeling. From a 3D seismic image, I first efficiently 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 final step, I also propose a convenient way to incorporate human interactions to obtain more accurate salt boundaries in especially complicated cases. I demonstrated the methods of computing salt likelihoods and salt boundaries using a 3D seismic image with multiple salt bodies.
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