Characterization of discrete fracture networks with deep-learning based hydrogeophysical inversion

Abstract

Characterization of fracture network is essential for understanding groundwater flow and solute transport, as well as waste storage. Deep-learning based ensemble smoother methods have proven to be effective in estimating hydraulic parameters in porous media. Compared to porous media, fracture fields are highly heterogeneous and typically non-Gaussian distributed, making the estimation of the fracture field from sparse borehole data extremely difficult. In this paper, we developed a joint hydrogeophysical inversion framework to improve the characterization of fracture networks. We first trained a convolutional variational autoencoder (CVAE) network to parameterize the fracture field, and then integrated with the ensemble smoother with multiple data assimilation (ESMDA) method to infer the fracture distribution by incorporating multiple datasets. Two numerical cases with different complexity were considered to assess the ability of the proposed joint inversion framework. The results show that the proposed hydrogeophyscial inversion framework can capture the main features of the fracture field. By integrating both the pressure and SP data, the fracture field can be reconstructed with an improved accuracy and reduced estimation uncertainty.