Complete identification of complex salt geometries from inaccurate migrated subsurface offset gathers using deep learning
This work addresses the time-consuming and error-prone manual interpretation of salt geometries in seismic imaging for geophysics, representing an incremental improvement by applying deep learning to a known bottleneck.
The authors tackled the problem of identifying salt inclusions in subsurface images by using a Convolutional Neural Network (U-Net) trained on synthetic data to predict correct salt masks from inaccurate migrated images, achieving high accuracy and good performance on unseen benchmark datasets.
Delimiting salt inclusions from migrated images is a time-consuming activity that relies on highly human-curated analysis and is subject to interpretation errors or limitations of the methods available. We propose to use migrated images produced from an inaccurate velocity model (with a reasonable approximation of sediment velocity, but without salt inclusions) to predict the correct salt inclusions shape using a Convolutional Neural Network (CNN). Our approach relies on subsurface Common Image Gathers to focus the sediments' reflections around the zero offset and to spread the energy of salt reflections over large offsets. Using synthetic data, we trained a U-Net to use common-offset subsurface images as input channels for the CNN and the correct salt-masks as network output. The network learned to predict the salt inclusions masks with high accuracy; moreover, it also performed well when applied to synthetic benchmark data sets that were not previously introduced. Our training process tuned the U-Net to successfully learn the shape of complex salt bodies from partially focused subsurface offset images.