Improving the Generalization of Deep Neural Networks in Seismic Resolution Enhancement

Seismic resolution enhancement is a key step for subsurface structure characterization. Although many have proposed the use of deep learning for resolution enhancement, these are typically hindered by the limitations in the application of synthetically trained networks onto real datasets. Domain adaptation (DA) offers an approach to reduce this disparity between training and inference data, aiming through the application of data transformations to bring the distributions of both data closer to each other. We propose a simple DA procedure, termed MLReal-Lite (the light version of the earlier introduced MLReal), that mainly relies on linear operations, namely convolution and correlation; these transformations introduce aspects of the field data into the synthetic data prior to training, and vice-versa with regards to the inference stage. Taking 1-D and 2-D resolution enhancement tasks as examples, we show how the inclusion of MLReal-Lite improves the performance of neural networks. Not only do the results demonstrate notable improvements in seismic resolution, they also exhibit a higher signal-to-noise ratio and better continuity of events, in comparison to the tests without MLReal-Lite. Finally, whilst illustrated on a resolution enhancement task, our proposed methodology is applicable for any seismic data of dimensions N-D, offering a DA applicable from well ties through to 3-D seismic volumes, and beyond.