Integration of DDPM and ILUES for Simultaneous Identification of Contaminant Source Parameters and Non‐Gaussian Channelized Hydraulic Conductivity Field

Integration of DDPM and ILUES for Simultaneous Identification of Contaminant Source Parameters and Non‐Gaussian Channelized Hydraulic Conductivity Field

Abstract Identifying highly channelized hydraulic conductivity fields and contaminant source parameters remains a challenging task, primarily due to the non‐Gaussian nature and high dimensionality of the parameter space, as well as the computational burden caused by repeatedly running forward numeri...

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Bibliographic Details
Main Authors: Xun Zhang, Simin Jiang, Na Zheng, Xuemin Xia, Zhi Li, Ruicheng Zhang, Jiangjiang Zhang, Xinshu Wang
Format: Article
Language:English
Published: Wiley 2024-09-01
Series:Water Resources Research
Subjects:
groundwater modeling
ensemble data assimilation
parameter identification
non‐Gaussian
denoising diffusion probabilistic models
surrogate model
Online Access:https://doi.org/10.1029/2023WR036893
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Summary:Abstract Identifying highly channelized hydraulic conductivity fields and contaminant source parameters remains a challenging task, primarily due to the non‐Gaussian nature and high dimensionality of the parameter space, as well as the computational burden caused by repeatedly running forward numerical models. This study proposes a novel deep learning parameterization method called AEdiffusion, which combines Diffusion Denoising Probabilistic Model (DDPM) with Variational Autoencoder (VAE) for dimensionality reduction. The method employs a generator‐refiner strategy to generate high‐dimensional aquifer properties from low‐dimensional latent representations. The inversion modeling was performed on a synthetic non‐Gaussian hydraulic conductivity field with line‐source contamination using the Iterative Local Updating Ensemble Smoother (ILUES) algorithm. The results demonstrate that the AEdiffusion‐ILUES framework can accurately identify model parameters. To reduce the computational burden, an AR‐Net‐WL (ARNW) surrogate model was introduced, resulting in an efficient inversion framework (AEdiffusion‐ILUES‐ARNW) with similar prediction accuracy and predictive uncertainty estimation as the AEdiffusion‐ILUES but at a lower computational cost.
ISSN:0043-1397
1944-7973

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