Deep Learning for Simultaneous Inference of Hydraulic and Transport Properties

Abstract

Identifying the heterogeneous conductivity field and reconstructing the contaminant release history are key aspects of subsurface remediation. Achieving these two goals with limited and noisy hydraulic head and concentration measurements is challenging. The obstacles include solving an inverse problem for high-dimensional parameters, and the high-computational cost needed for the repeated forward modeling. We use a convolutional adversarial autoencoder (CAAE) for the parameterization of the heterogeneous non-Gaussian conductivity field with a low-dimensional latent representation. Additionally, we trained a three-dimensional dense convolutional encoder-decoder (DenseED) network to serve as the forward surrogate for the flow and transport processes. Combining the CAAE and DenseED forward surrogate models, the ensemble smoother with multiple data assimilation (ESMDA) algorithm is used to sample from the Bayesian posterior distribution of the unknown parameters, forming a CAAE-DenseED-ESMDA inversion framework. We applied this CAAE-DenseED-ESMDA inversion framework in a three-dimensional contaminant source and conductivity field identification problem. A comparison of the inversion results from CAAE-ESMDA with physical flow and transport simulator and CAAE-DenseED-ESMDA is provided, showing that accurate reconstruction results were achieved with a much higher computational efficiency.

References in corpus (17)

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Auto-Encoding Variational Bayes
• Generative Adversarial Networks
• Training-image based geostatistical inversion using a spatial generative adversarial neural network
• Deep autoregressive neural networks for high-dimensional inverse problems in groundwater contaminant source identification
• Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network
• Geological realism in hydrogeological and geophysical inverse modeling: a review
• Deep-learning-based surrogate flow modeling and geological parameterization for data assimilation in 3D subsurface flow
• Integration of adversarial autoencoders with residual dense convolutional networks for estimation of non-Gaussian hydraulic conductivities
• An iterative local updating ensemble smoother for estimation and uncertainty assessment of hydrologic model parameters with multimodal distributions
• Multiphase flow prediction with deep neural networks
• Deep generative models in inversion: a review and development of a new approach based on a variational autoencoder
• Gradient-based deterministic inversion of geophysical data with Generative Adversarial Networks: is it feasible?
• Markov Chain Monte Carlo with Neural Network Surrogates: Application to Contaminant Source Identification
• Bayesian multiscale deep generative model for the solution of high-dimensional inverse problems
• Learning on dynamic statistical manifolds
• Information-geometry of physics-informed statistical manifolds and its use in data assimilation

Cited by in corpus (1)

• Transfer Learning on Multi-Dimensional Data: A Novel Approach to Neural Network-Based Surrogate Modeling