An Improved Tandem Neural Network Architecture for Inverse Modeling of Multicomponent Reactive Transport in Porous Media

Abstract

Abstract Parameter estimation for reactive transport models (RTMs) is important in improving their predictive capacity for accurately simulating subsurface hydrogeochemical processes. This paper introduces a deep learning approach called the tandem neural network architecture (TNNA), which consists of a forward network and a reverse network to estimate input parameters for RTMs. The TNNA approach has a limitation in that the approximation error from the forward network often results in biased inversion results. To solve this problem, we proposed to enhance TNNA using an adaptive updating strategy (AUS), which locally reduces the approximation error of the forward network. The developed framework updates the forward network by iteratively using local sampling and transfer learning. The TNNA‐AUS was verified by a cation exchange example. The results show that TNNA‐AUS successfully reduces the inversion bias and improves the computational efficiency and inversion accuracy, compared with the global improvement strategy of adding training samples according to the prior distribution of model parameters. After verification, the TNNA‐AUS was applied to a real‐world and well‐documented RTM problem of the Aquia aquifer, Maryland, USA. The inversion results demonstrate that the developed TNNA‐AUS algorithm is an excellent tool for us to understand the complex subsurface hydrogeochemical processes and estimate the associated reaction parameters.