Groundwater contamination source identification and high-dimensional parameter inversion using residual dense convolutional neural network

•Deep learning and ensemble-based method are coupled for efficient data assimilation.•The residual learning is combined with densely-connected convolutional neural network to construct surrogate for groundwater contaminant transport model.•ILUES is used for joint inversion high-dimensional parameters using RDCNN-based surrogate model.•The influence of main parameters of RDCNN on the prediction accuracy is analyzed. Data assimilation for high-dimensional parameter joint inversion of multiple time-varying source strength and hydraulic conductivity fields can be computationally intensive as a large number of forward model runs are usually required. In this study, a deep neural network-based surrogate is proposed to replace the forward model in the data assimilation process to efficiently achieve high-dimensional parameter inversion. The deep convolutional encoding-decoding network architecture is used to leverage the advantages of the convolutional network in processing image-like data, where the high-dimensional input and output fields of the forward model are expressed as images. The encoding network extracts the main features of the multiple time-varying source input image and high-dimensional hydraulic conductivity fields, and the decoding network refines the extracted features to generate the output hydraulic head and contaminant concentration fields. The Residual Dense Convolutional Neural Network (RDCNN) is proposed by combining residual learning with a densely-connected convolutional neural network to solve the image regression problem for surrogate construction. Iterative local updating ensemble smoother (ILUES) is utilized to assimilate hydraulic head and contaminant concentration data to inverse high-dimensional parameters. The proposed method is evaluated by jointly inversing the high-dimensional hydraulic conductivity and source parameters for a synthetic multiple-source contaminated aquifer. The results indicate that compared to the surrogate constructed by Dense Convolutional Neural Network (DCNN) without addressing residual learning, the RDCNN surrogate captures the model input-output relationship better with relatively high training efficiency. The RDCNN surrogate-coupled ILUES achieves state-of-the-art performance in terms of inversion accuracy of 9 pollution source parameters and 255 hydraulic conductivity field parameters and computational efficiency in comparison to the ILUES based on the forward model.