Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network
TLDR
This work uses a deep neural network of the variational autoencoder type to construct a parametric low-dimensional base model parameterization of complex binary geological media and finds that the dimensionality reduction (DR) approach outperforms principle component analysis (PCA), optimization-PCA, and discrete cosine transform (DCT) DR techniques for unconditional geostatistical simulation of a channelized prior model.About:
This article is published in Advances in Water Resources.The article was published on 2017-12-01 and is currently open access. It has received 165 citations till now. The article focuses on the topics: Autoencoder & Parametric statistics.read more
Citations
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A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists
TL;DR: It is argued that DL can help address several major new and old challenges facing research in water sciences such as interdisciplinarity, data discoverability, hydrologic scaling, equifinality, and needs for parameter regionalization.
Journal ArticleDOI
A trans-disciplinary review of deep learning research for water resources scientists.
TL;DR: In this paper, the authors provide water resources scientists and hydrologists with a simple technical overview, trans-disciplinary progress update, and a source of inspiration about the relevance of deep learning to water.
Journal ArticleDOI
Training-Image Based Geostatistical Inversion Using a Spatial Generative Adversarial Neural Network
Abstract: Probabilistic inversion within a multiple‐point statistics framework is often computationally prohibitive for high‐dimensional problems. To partly address this, we introduce and evaluate a new training‐image based inversion approach for complex geologic media. Our approach relies on a deep neural network of the generative adversarial network (GAN) type. After training using a training image (TI), our proposed spatial GAN (SGAN) can quickly generate 2‐D and 3‐D unconditional realizations. A key characteristic of our SGAN is that it defines a (very) low‐dimensional parameterization, thereby allowing for efficient probabilistic inversion using state‐of‐the‐art Markov chain Monte Carlo (MCMC) methods. In addition, available direct conditioning data can be incorporated within the inversion. Several 2‐D and 3‐D categorical TIs are first used to analyze the performance of our SGAN for unconditional geostatistical simulation. Training our deep network can take several hours. After training, realizations containing a few millions of pixels/voxels can be produced in a matter of seconds. This makes it especially useful for simulating many thousands of realizations (e.g., for MCMC inversion) as the relative cost of the training per realization diminishes with the considered number of realizations. Synthetic inversion case studies involving 2‐D steady state flow and 3‐D transient hydraulic tomography with and without direct conditioning data are used to illustrate the effectiveness of our proposed SGAN‐based inversion. For the 2‐D case, the inversion rapidly explores the posterior model distribution. For the 3‐D case, the inversion recovers model realizations that fit the data close to the target level and visually resemble the true model well.
Journal ArticleDOI
Application of deep learning algorithms in geotechnical engineering: a short critical review
TL;DR: This study presented the state of practice of DL in geotechnical engineering, and depicted the statistical trend of the published papers, as well as describing four major algorithms, including feedforward neural, recurrent neural network, convolutional neural network and generative adversarial network.
Journal ArticleDOI
Deep Convolutional Encoder-Decoder Networks for Uncertainty Quantification of Dynamic Multiphase Flow in Heterogeneous Media
TL;DR: In this paper, a deep convolutional encoder-decoder neural network was proposed to characterize the high-dimensional time-dependent outputs of the dynamic multi-phase flow model with a 2500-dimensional stochastic permeability field.
References
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