Hydraulic heterogeneity estimation with transient hydraulic tomography and convolutional encoder-decoder neural network

This study proposes a hydraulic tomography neural network (HT-NN) based on a convolutional encoder-decoder neural network (DenseNet) combined with a head data sampling strategy to estimate hydrogeological parameter fields. Numerical experiments demonstrate that HT-NN effectively captures the spatial...

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Bibliographic Details
Published in:Stochastic environmental research and risk assessment Vol. 39; no. 9; pp. 4083 - 4105
Main Authors: Chen, Yu-Kai, Tsai, Jui-Pin, Wang, Bo-Tsen, Chang, Chia-Hao
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2025
Subjects:
Aquatic Pollution
Chemistry and Earth Sciences
Computational Intelligence
Computer Science
Earth and Environmental Science
Earth Sciences
Environment
Math. Appl. in Environmental Science
Original Paper
Physics
Probability Theory and Stochastic Processes
Statistics for Engineering
Waste Water Technology
Water Management
Water Pollution Control
Parameter field estimation
Deep learning
Hydraulic tomography
Head data sampling strategy
Convolutional neural network
ISSN:1436-3240, 1436-3259
Online Access:Get full text
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Summary:This study proposes a hydraulic tomography neural network (HT-NN) based on a convolutional encoder-decoder neural network (DenseNet) combined with a head data sampling strategy to estimate hydrogeological parameter fields. Numerical experiments demonstrate that HT-NN effectively captures the spatial characteristics of both hydraulic conductivity ( ) and specific storage ( ) fields and achieves high accuracy in delineating subsurface heterogeneity. By selecting late- and early-time head data to construct the input matrix, HT-NN substantially improves parameter estimation while significantly reducing computational time. Compared to the successive linear estimator (SLE), HT-NN achieves more accurate parameter estimation and reduces computation time from 28.5 h to 0.76 s. The simulated heads derived from HT-NN’s estimated parameter fields closely match the reference heads across all experiments. Additionally, adopting a smaller input matrix with a simplified encoder-decoder structure greatly enhances computational efficiency while maintaining estimation accuracy. These findings demonstrate the potential of HT-NN as an efficient and reliable alternative for estimating hydrogeological parameters in heterogeneous aquifer systems.
ISSN:1436-3240
1436-3259
DOI:10.1007/s00477-025-03051-8