Joint identification of contaminant source characteristics and hydraulic conductivity in a tide-influenced coastal aquifer

Dodangeh, Arezou; Rajabi, Mohammad Mahdi; Carrera, Jesús; Fahs, Marwan

doi:10.1016/j.jconhyd.2022.103980

Cited by 10 publications

(4 citation statements)

References 47 publications

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“…To overcome these limitations, researchers can employ indirect observational data, such as hydraulic head (Yoon et al, 2017), solute concentration (Dodangeh et al, 2022), temperature (Blanco-Coronas et al, 2021), and electrical resistivity tomography (ERT; González-Quirós & Comte, 2020), and leverage data assimilation (DA) techniques for effective characterization of heterogeneous aquifers (Goebel et al, 2017;Sendrós et al, 2021). Among the various data types, hydraulic head, particularly the transient measurement, stands out for its ability of capturing fluid flow characteristics and thereby enriching aquifer characterization (P. Kang et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning-Based Data Assimilation Approach to Characterizing Coastal Aquifers Amid Non-linearity and Non-Gaussianity Challenges

Cao,

Zhang,

Gan

et al. 2024

Preprint

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Seawater intrusion (SI) 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 SI predictions, potentially diminishing their effectiveness in management decisions. Data assimilation (DA) offers a solution by integrating various types of observational data with the model to characterize heterogeneous coastal aquifers. Traditional DA techniques, like ensemble smoother using the Kalman formula (ES) 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 ES, aimed at effectively characterizing coastal aquifers with varying levels of heterogeneity. We systematically investigate a range of factors that impact the performance of ES, including the number and types of observations, the degree of aquifer heterogeneity, the structure and training options of the DL models. Our findings reveal that ES excels in characterizing heterogeneous aquifers under non-linear and non-Gaussian conditions. Comparison between ES and ES under different experimentation settings underscores the robustness of ES. Conversely, in certain scenarios, ES displays noticeable biases in the characterization results, especially when measurement data from non-linear and discontinuous processes are used. To optimize the efficacy of ES, attention must be given to the design of the DL model and the selection of observational data, which are crucial to ensure the universal applicability of this DA method.

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Section: Introductionmentioning

confidence: 99%

A Deep Learning-Based Data Assimilation Approach to Characterizing Coastal Aquifers Amid Non-linearity and Non-Gaussianity Challenges

Cao,

Zhang,

Gan

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…To overcome these limitations, researchers can employ indirect observational data, such as hydraulic head (Yoon et al., 2017), solute concentration (Dodangeh et al., 2022), temperature (Blanco‐Coronas et al., 2021), and electrical resistivity tomography (ERT; González‐Quirós & Comte, 2020), and leverage data assimilation (DA) techniques for effective characterization of heterogeneous aquifers (Goebel et al., 2017; Sendrós et al., 2021). Among the various data types, hydraulic head, particularly the transient measurement, stands out for its ability of capturing fluid flow characteristics and thereby enriching aquifer characterization (P. Kang et al., 2017).…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning‐Based Data Assimilation Approach to Characterizing Coastal Aquifers Amid Non‐Linearity and Non‐Gaussianity Challenges

Cao,

Zhang,

Gan

et al. 2024

Water Resources Research

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Seawater intrusion (SI) 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 SI predictions, potentially diminishing their effectiveness in management decisions. Data assimilation (DA) offers a solution by integrating various types of observational data with the model to characterize 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. Our findings reveal that ESDL excels in characterizing heterogeneous aquifers under non‐linear and 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 characterization results, especially when measurement data from non‐linear and discontinuous processes are used. To optimize the efficacy of ESDL, attention must be given to the design of the DL model and the selection of observational data, which are crucial to ensure the universal applicability of this DA method.

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“…However, the heterogeneity of aquifer properties is intrinsic in natural world. A valid representation of subsurface heterogeneity is necessary for accurately modeling contaminate plumes or hydraulic head distributions (Dodangeh et al., 2022; Pedretti et al., 2014; Siirila‐Woodburn & Maxwell, 2015; Zhan et al., 2022). Simulating subsurface processes in heterogeneous media always results in high‐dimensional model calibration problems.…”

Section: Introductionmentioning

confidence: 99%

Integration of Deep Learning and Information Theory for Designing Monitoring Networks in Heterogeneous Aquifer Systems

Chen

Dai

Dong

et al. 2022

Water Resources Research

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Groundwater monitoring networks are direct sources of information for revealing subsurface system dynamic processes. However, designing such networks is difficult due to uncertainties in the spatial heterogeneity of aquifer parameters such as permeability (k). This study combines deep learning and information theory with an optimization framework to address network design problems in heterogeneous aquifer systems. The framework first employs a generative adversarial network to parameterize heterogeneous k distribution using a low-dimensional latent representation. Then, surrogate models are developed based on the deep neural networks to perform uncertainty quantification of pressure heads and solute concentrations at locations of pre-designed candidate monitoring stations. The monitoring stations are then ranked using the greedy search algorithm based on the maximum information minimum redundancy (MIMR) criterion. In order to depict the importance of each candidate monitoring location, the hotspot maps of the selection probability (P s ) are derived from MIMR repetition results. Comprehensive monitoring networks derived from the hotspot maps are then conducted as the final monitoring stations to improve monitoring information compared to MIMR results. Additionally, nine entropy quantization strategies are compared to evaluate their effects on monitoring network optimization results. Results indicate that caution should be taken when selecting entropy quantization strategies to achieve the accuracy required for model calibration and to improve the efficiency of monitoring optimization. Considering high-dimensional uncertainties associated with aquifer parameters, the developed framework can provide important insights for monitoring network designs in various earth observational projects. Plain Language SummaryGroundwater is widely distributed throughout the crust and lithosphere playing a critical role in geological evolution processes and environmental problems. This study presents an integrated framework for designing groundwater monitoring networks in heterogeneous aquifer systems. The proposed model takes advantage of the strength of deep-learning algorithms and deals with high-dimensional and highly non-linear problems of entropy-based methods for designing the monitoring networks. The framework can efficiently provide monitor locations for complex aquifers. The monitoring data collected by the designed network can significantly reduce uncertainties associated with high-dimensional parameters and contain the least amount of redundant information. Further, the proposed method is significant in practice as credible groundwater forecasting requires model calibrations that use monitoring data.

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Joint identification of contaminant source characteristics and hydraulic conductivity in a tide-influenced coastal aquifer

Cited by 10 publications

References 47 publications

A Deep Learning-Based Data Assimilation Approach to Characterizing Coastal Aquifers Amid Non-linearity and Non-Gaussianity Challenges

A Deep Learning-Based Data Assimilation Approach to Characterizing Coastal Aquifers Amid Non-linearity and Non-Gaussianity Challenges

A Deep Learning‐Based Data Assimilation Approach to Characterizing Coastal Aquifers Amid Non‐Linearity and Non‐Gaussianity Challenges

Integration of Deep Learning and Information Theory for Designing Monitoring Networks in Heterogeneous Aquifer Systems

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