..., followed by application of the method to a travel-time tomography inverse problem to investigate its model updating performance. neural network resolution architecture united states government artificial intelligence autoencoder application spatial adaptivity deep learning machine learning...

... with variational autoencoder (VAE) architecture to enable improved representation of complex spatial patterns and provide some degree of interpretability by allowing certain spatial features and attributes of a property map to be controlled by a single latent variable (generative factor), while remaining...

... convolutional autoencoder to extract the relevant features of 4D images and generate a reduced representation of the data. The architecture of the autoencoder is based on the well-known VGG-19 network, from which we take advantage of the transfer learning technique. Using a pre-trained model bypasses the need...

... for geologically consistent feature-based model updating, and (ii) efficient prediction of the statistical information that are required for model updating. The proposed method uses deep convolutional autoencoders to nonlinearly map the original complex and high-dimensional parameters onto a low-dimensional...

... learning autoencoder networks to extract salient transient features from pressure/stress fields and bulks of production data. The data-driven model is demonstrated on three illustrative examples involving single and two-phase coupled flow/geomechanics simulations and a real production dataset from Vaca...