Deep Learning Strategy for Salt Model Building

Velocity models are crucial intermediate products generated in seismic data processing, and the model’s accuracy is essential for constructing quality seismic images. Conventional approaches to velocity-model building (VMB) use a family of inversion methods, among which are ray-based tomography and full-waveform inversion. These methods have been highly optimized throughout the years but are still heavily dependent on continuous human curation of the results, which leads to an overall high time cost, especially in areas with high structural complexity, such as those containing salt tectonics. We have investigated a deep learning (DL) approach that accurately defines salt geometries for VMB. We train our convolutional neural network on synthetic shot gather data, explore a manner of leveraging information through summation of shot data, and determine the influence that the choice of loss function has on the quality and aspect of predicted velocity models. Our residual U-Net model trained on data containing only randomly shaped salt bodies can estimate geologically complex salt geometries such as those in 2D SEG/EAGE salt model slices. Our results find that deeper encoder-decoder models with shortcut connections resolve velocity model structures better than shallower models. Moreover, network models trained with a composite loss function — combining mean absolute error and the multiscale structural similarity index — better delineate the contours of areas with high-velocity contrast and better recover regions with a uniform velocity trend than network models trained with conventional loss functions like the mean squared error. The residual U-Net and loss functions that we use are not task-specific and can be extended to other DL approaches to VMB.