Deep Learning for Simultaneous Inference of Hydraulic and Transport Properties

Identification of a heterogeneous conductivity field and reconstruction of a contaminant release history are key aspects of subsurface remediation. These two goals are achieved by combining model predictions with sparse and noisy hydraulic head and concentration measurements. Solution of this invers...

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Bibliographic Details
Published in:Water resources research Vol. 58; no. 10
Main Authors: Zhou, Zitong, Zabaras, Nicholas, Tartakovsky, Daniel M.
Format: Journal Article
Language:English
Published: Washington John Wiley & Sons, Inc 01.10.2022
American Geophysical Union (AGU)
Subjects:
adversarial
Algorithms
Bayesian analysis
Bayesian theory
Coders
Computational efficiency
Computer applications
Computing costs
Conductivity
Contaminants
Data assimilation
Data collection
Deep learning
History
Hydraulics
Identification
inverse problem
Inverse problems
Machine learning
Mathematical models
neural network
Parameters
Piezometric head
Probability theory
Reconstruction
Remediation
Simulators
Simultaneous discrimination learning
source indentification
subsurface
Transport
Transport properties
water
ISSN:0043-1397, 1944-7973
Online Access:Get full text
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Summary:Identification of a heterogeneous conductivity field and reconstruction of a contaminant release history are key aspects of subsurface remediation. These two goals are achieved by combining model predictions with sparse and noisy hydraulic head and concentration measurements. Solution of this inverse problem is notoriously difficult due to, in part, high dimensionality of the parameter space and high computational cost of repeated forward solves. We use a convolutional adversarial autoencoder (CAAE) to parameterize a heterogeneous non‐Gaussian conductivity field via a low‐dimensional latent representation. A three‐dimensional dense convolutional encoder‐decoder (DenseED) network serves as a forward surrogate of the flow and transport model. The CAAE‐DenseED surrogate is fed into the ensemble smoother with multiple data assimilation (ESMDA) algorithm to sample from the Bayesian posterior distribution of the unknown parameters, forming a CAAE‐DenseED‐ESMDA inversion framework. The resulting CAAE‐DenseED‐ESMDA inversion strategy is used to identify a three‐dimensional contaminant source and conductivity field. A comparison of the inversion results from CAAE‐ESMDA with physical flow and transport simulator and from CAAE‐DenseED‐ESMDA shows that the latter yields accurate reconstruction results at the fraction of the computational cost of the former. Plain Language Summary Identification of a heterogeneous conductivity field and reconstruction of a contaminant release history are key aspects of subsurface remediation. These two goals are achieved by combining model predictions with sparse and noisy hydraulic head and concentration measurements. Solution of this inverse problem is notoriously difficult due to, in part, high dimensionality of the parameter space and high computational cost of repeated forward solves. We develop a deep‐learning strategy to identify a three‐dimensional contaminant source and conductivity field from sparse observations. Key Points Our deep‐learning strategy reconstructs three‐dimensional conductivity field and contaminant release history Conductivity parameterization with convolutional adversarial autoencoder reduces the inverse problem's dimensionality Convolutional encoder‐decoder acts as a surrogate of forward models; ensemble smoother approximates parameters' posterior distribution
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USDOE
ISSN:0043-1397
1944-7973
DOI:10.1029/2021WR031438