Supervised learning to detect salt body

In this paper we present a novel approach to detect salt bodies based on seismic attributes and supervised learning. We report on the use of a machine learning algorithm, Extremely Randomized Trees, to automatically identify and classify salt regions. We have worked with a complex synthetic seismic dataset from phase I model of the SEG Advanced Modeling Corporation (SEAM) that corresponds to deep water regions of the Gulf of Mexico. This dataset has very low frequency and contains sediments bearing amplitude values similar to those of salt bodies. In the first step of our methodology, where machine learning is applied directly to the seismic data, we obtained accuracy values of around 80%. A second (post-processing) smoothing step improved accuracy to around 95%. We conclude that machine learning is a promising mechanism to identify salt bodies on seismic data, especially with models that can produce complex decision boundaries, while being able to control the associated variance component of error.