Abstract

One of the most important steps in velocity model building for seismic imaging in salt basins such as the Gulf of Mexico is the iterative refinement of the salt geometry. Traditionally, this step is difficult to automate, and production workflows require extensive domain expert intervention to accurately interpret the salt bodies on images migrated with an incorrect intermediate velocity model. To alleviate this problem, we propose an end-to-end semisupervised deep learning pipeline, SaltNet, capable of fully automated salt interpretation during initial model building iterations. We show that the method can be used to build the initial salt model (top of salt-1 and base of salt-1 or salt body-1 iterations) without domain expert intervention while achieving accuracy close to that of a human expert. Unlike existing convolutional neural network (CNN)-based salt interpretation applications, this method is designed to work on noisy low-resolution real-data seismic images that are typically encountered during the initial model building stage. It is also generalizable to migrated images from previously unseen surveys. This is achieved by training a suite of deep high-capacity CNN models with a multiview semisupervised learning scheme that leverages data and model distillation concepts to make these models robust to potentially large domain differences that images from a new target survey may exhibit. Consequently, CNN models achieve human-level interpretation accuracy on such new surveys without the need to manually interpret any portion of the target survey. Results from a field test on a Gulf of Mexico survey show excellent agreement between migrated images generated by the conventional interpreter-picked and SaltNet-picked initial salt model.

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