Characterization of Heterogeneous Coastal Aquifers Using A Deep
Learning-Based Data Assimilation Approach
Abstract
Seawater intrusion poses a substantial threat to water security in
coastal regions, where numerical models play a pivotal role in
supporting groundwater management and protection. However, the inherent
heterogeneity of coastal aquifers introduces significant uncertainties
into model predictions, potentially diminishing their effectiveness in
management decisions. Data assimilation (DA) offers a solution by
incorporating various types of observational data to characterize these
heterogeneous coastal aquifers. Traditional DA techniques, like ensemble
smoother using the Kalman formula (ESK) and Markov chain Monte Carlo,
face challenges when confronted with the non-linearity, non-Gaussianity,
and high-dimensionality issues commonly encountered in aquifer
characterization. In this study, we introduce a novel DA approach rooted
in deep learning (DL), referred to as ESDL, aimed at effectively
characterizing coastal aquifers with varying levels of heterogeneity. We
systematically investigate a range of factors that impact the
performance of ESDL, including the number and types of observations, the
degree of aquifer heterogeneity, the structure and training options of
the DL models, etc. Our findings reveal that ESDL excels in
characterizing heterogeneous aquifers, particularly when faced with
non-Gaussian conditions. Comparison between ESDL and ESK under different
experimentation settings underscores the robustness of ESDL. Conversely,
in certain scenarios, ESK displays noticeable biases in the
characterizing results, especially when measurement data from nonlinear
and discontinuous processes are used. To optimize the efficacy of ESDL,
meticulous attention must be given to the design of the DL model and the
selection of training options, which are crucial to ensure the universal
applicability of this DA method.