Hydraulic heterogeneity estimation with transient hydraulic tomography and convolutional encoder-decoder neural network
Journal
Stochastic Environmental Research and Risk Assessment
Journal Volume
39
Journal Issue
9
Start Page
4083
End Page
4105
ISSN
1436-3240
1436-3259
Date Issued
2025-07-29
Author(s)
Abstract
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.
Subjects
Hydraulic tomography
Convolutional neural network
Parameter field estimation
Deep learning
Head data sampling strategy
SDGs
Publisher
Springer Science and Business Media LLC
Type
journal article