Enhancing Coastal Aquifer Characterization and Contamination Inversion with Deep Learning

Coastal aquifers are critical freshwater resources that face increasing threats from contamination and saltwater intrusion. Traditional approaches for characterizing these aquifers are challenged by complex dynamics, high-dimensional parameter spaces, and significant computational demands. This study presents an innovative method that combines an Auto-Regressive Convolutional Neural Network (AR-CNN) surrogate model with the Iterative Local Updating Ensemble Smoother (ILUES) for the joint inversion of contamination source parameters and hydraulic conductivity fields. The AR-CNN surrogate model, trained on synthetic data generated by the SEAWAT model, effectively approximates the complex input&ndash:output relationships of coastal aquifer systems, substantially reducing computational burden. The ILUES framework utilizes observational data to iteratively update model parameters. A case study involving a heterogeneous coastal aquifer with multipoint pollution sources demonstrates the efficacy of the proposed method. The results indicate that AR-CNN-ILUES successfully estimates pollution source strengths and characterizes the hydraulic conductivity field, although some limitations are observed in areas with sparse monitoring points and complex geological structures. Compared to the traditional SEAWAT-ILUES framework, the AR-CNN-ILUES approach reduces the total inversion time from approximately 70.4 h to 16.2 h, improving computational efficiency by about 77%. These findings highlight the potential of the AR-CNN-ILUES framework as a promising tool for efficient and accurate characterization of coastal aquifers. By enhancing computational efficiency without significantly compromising accuracy, this method offers a viable solution for the sustainable management and protection of coastal groundwater resources.