Deep-salt: Complete three-dimensional salt segmentation from inaccurate migrated subsurface offset gathers using deep learning

Delimiting salt inclusions from migrated images during the velocity model building flow is a time-consuming activity that depends on highly human-curated analysis and is subject to interpretation errors or limitations of the images and methods available. We propose a supervised deep learning based method to include three-dimensional salt geometries in the velocity models. We compare two convolutional networks – based on the U-Net architecture – which can process three-dimensional seismic data. One architecture uses three-dimensional convolutional kernels, and the other has convolutional long short-term memory units. Each architecture requires specific preprocessing steps which affects their training and predictive performance. Both proposed architectures use subsurface offset gathers obtained from reverse time migration with an extended imaging condition as input and are trained to predict the salt inclusions. The velocity model used in migration is a reasonable approximation of sediment velocity but without salt inclusions. Thus, the migration model and, consequently, the migrated images are inaccurate due to the absence of the salt inclusion in the model using just the sediment velocity information for the segmentation. A similar salt inclusion methodology was previously validated for two-dimensional approaches; we extend it to the three-dimensional case. Our approach relies on subsurface common image gathers to focus the sediments' reflections around the zero offset and spread salt reflections' energy over large subsurface offsets. The results show that both proposed network models can accurately delineate the salt bodies in the test models, but when evaluating the trained networks for the three-dimensional SEG/EAGE salt model, the architecture with convolutional long short-term memory units has proven to generalize better.