Give Geological Context to Seismic Attributes Through Artificial Intelligence, Using Neural Style Transfer

In the oil and gas industry, seismic attributes are used to study and understand the subsurface geology. However, their usefulness is limited by a lack of sufficient geological context. In this work, seismic attributes (spectral decomposition) are put into a geological context using the neural style transfer (NST) algorithm to visualize a paleoriver system.

To transfer the style from the reference image to the content image, the stylized image is initialized with the content image, and the total loss is optimized with respect to the pixels of the stylized image. Adam optimizer is used and the content weight and style weight can be adjusted to control the relative importance of the content and style in the final stylized image.

The output image demonstrates how the stratigraphic feature highlighted by the spectral decomposition attribute would appear if it were captured from a satellite image today. This output image is easy to understand for anyone, with none to low expertise in geoscience. Neural style transfer can be a valuable tool for analyzing and visualizing stratigraphic systems.