Physics-Informed Convolutional Decoder (PICD): A Novel Approach for Direct Inversion of Heterogeneous Subsurface Flow

Wang, Nanzhe1,2; Kong, Xiang-Zhao2; Zhang, Dongxiao3,4

2024-07-16

10.1029/2024GL108163

GEOPHYSICAL RESEARCH LETTERS   影响因子和分区

0094-8276

1944-8007

["We propose a physics-informed convolutional decoder (PICD) framework for inverse modeling of heterogenous groundwater flow. PICD stands out as a direct inversion method, eliminating the need for repeated forward model simulations. The framework combines data-driven and physics-driven approaches by integrating monitoring data and domain knowledge into the inversion process. PICD utilizes a convolutional decoder to effectively approximate the spatial distribution of hydraulic heads, while Karhunen-Lo & egrave;ve expansion (KLE) is employed to parameterize hydraulic conductivities. During the training process, the stochastic vector in KLE and the parameters of the convolutional decoder are adjusted simultaneously to minimize the data-mismatch and the physical violation. The final optimized stochastic vectors correspond to the estimation of hydraulic conductivities, and the trained convolutional decoder can predict the evolution and distribution of hydraulic heads. Various scenarios of groundwater flow are examined and results demonstrate the framework's capability to accurately estimate heterogeneous hydraulic conductivities and to deliver satisfactory predictions of hydraulic heads, even with sparse measurements.","Inverse modeling refers to estimate the unknown model parameters with measurements of model responses. In groundwater flow problems, the information about subsurface formation parameters is very limited, so inverse modeling is required to inference the uncertain formation parameters with sparse measurements. Many conventional inversion methods necessitate repeated forward calculations to compare the predictions with measurements and evaluate the likelihood of different estimations, resulting in a substantial computational burden. In this work, we propose a novel physics-informed convolutional decoder (PICD) framework, which, as a direct inversion method, can circumvent the need for multiple forward calculations during the inversion process. In addition to measurements, physical laws are leveraged to provide extra information for inversion, alleviating the dependence on data, and enforcing the predictions align with measurements as well as domain-specific knowledge. Several groundwater flow problems are considered to validate the effectiveness of the proposed PICD framework, and satisfactory performance can be obtained. The proposed PICD framework emerges as a promising tool for efficient and informed groundwater flow inverse modeling.","A physics-informed deep learning framework is proposed for inversion of groundwater flow Inversion can be performed directly without iterative forward modeling Satisfactory inversion performance can be achieved even with sparse measurements"]

physics-informed convolutional decoder inversion method subsurface flow

SCI ; EI

英语

通讯

National Natural Science Foundation of China[52288101]

Geology

Geosciences, Multidisciplinary

WOS:001265910500007

AMER GEOPHYSICAL UNION

20242816686786

Convolution ; Decoding ; Groundwater ; Hydraulic conductivity ; Inverse problems ; Stochastic systems

Groundwater:444.2 ; Fluid Flow, General:631.1 ; Hydraulics:632.1 ; Information Theory and Signal Processing:716.1 ; Data Processing and Image Processing:723.2 ; Control Systems:731.1 ; Systems Science:961

GEOSCIENCES

Web of Science

1.Stanford Univ, Dept Energy Sci & Engn, Palo Alto, CA USA
2.Swiss Fed Inst Technol, Inst Geophys, Geothermal Energy & Geofluids Grp, Zurich, Switzerland
3.Eastern Inst Adv Study, Eastern Inst Technol, Ningbo, Peoples R China
4.Southern Univ Sci & Technol, Natl Ctr Appl Math Shenzhen NCAMS, Shenzhen, Peoples R China

Wang, Nanzhe,Kong, Xiang-Zhao,Zhang, Dongxiao. Physics-Informed Convolutional Decoder (PICD): A Novel Approach for Direct Inversion of Heterogeneous Subsurface Flow[J]. GEOPHYSICAL RESEARCH LETTERS,2024,51(13).

Wang, Nanzhe,Kong, Xiang-Zhao,&Zhang, Dongxiao.(2024).Physics-Informed Convolutional Decoder (PICD): A Novel Approach for Direct Inversion of Heterogeneous Subsurface Flow.GEOPHYSICAL RESEARCH LETTERS,51(13).

Wang, Nanzhe,et al."Physics-Informed Convolutional Decoder (PICD): A Novel Approach for Direct Inversion of Heterogeneous Subsurface Flow".GEOPHYSICAL RESEARCH LETTERS 51.13(2024).