Physics-constrained deep learning for data assimilation of subsurface transport
Haiyi Wu, Rui Qiao
Abstract
Data assimilation of subsurface transport is important in many energy and environmental applications, but its solution is typically challenging. In this work, we build physics-constrained deep learning models to predict the full-scale hydraulic conductivity, hydraulic head, and concentration fields in porous media from sparse measurement of these observables. The model is developed based on convolutional neural networks with the encoding-decoding process. The model is trained by minimizing a loss function that incorporates residuals of governing equations of subsurface transport instead of using labeled data. Once trained, the model predicts the unknown conductivity, hydraulic head, and concentration fields with an average relative error <10% when the data of these observables is available at 12.2% of the grid points in the porous media. The model has a robust predictive performance for porous media with different conductivities and transport under different Péclet number (0.5 < Pe < 500). We also quantify the predictive uncertainty of the model and evaluate the reliability of its prediction by incorporating a variational parameter into the model.