Generating Realistic Geology Conditioned on Physical Measurements with Generative Adversarial Networks

Understand

An important problem in geostatistics is to build models of the subsurface of the Earth given physical measurements at sparse spatial locations.

• Typically, this is done using spatial interpolation methods or by reproducing patterns from a reference image.
• However, these algorithms fail to produce realistic patterns and do not exhibit the wide range of uncertainty inherent in the prediction of geology.
• In this paper, we show how semantic inpainting with Generative Adversarial Networks can be used to generate varied realizations of geology which honor physical measurements while matching the expected geological patterns.