Physics-informed W-Net GAN for the direct stochastic inversion of fullstack seismic data into facies models
Roberto Miele,
Leonardo Azevedo
Abstract:Predicting the spatial distribution of geological facies in the subsurface from fullstack geophysical data is a main step in the geo-modeling workflow for energy exploration and environmental tasks and requires solving an inverse problem. Generative adversarial networks (GAN) have shown great potential for geologically accurate inverse modeling, although with limitations in computational costs and in accounting for uncertainty in the prediction of facies-dependent properties. To overcome this limitation, we pr… Show more
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