Densely Connected Squeeze‐and‐Excitation Convolutional Encoder‐Decoder Networks for Identifying Preferential Channels in Highly Heterogeneous Porous Media

Abstract

AbstractIn highly heterogeneous formations, the preferential channels (PC) that usually exist can dramatically change the transport dynamics of a solute plume. Identifying PC is vital to delineate contaminant transport and conduct the environmental risk analysis. Conventional methods usually depend on the solution of the governing equations for flow and transport and struggle with high computational costs. In this paper, we transform PC identification into a particular semantic segmentation task for the first time and build a densely connected squeeze‐and‐excitation convolutional encoder‐decoder network. A training strategy combining segmentation loss and new regression loss (index distance loss) is used to improve the identification of PC locations. We evaluate the effectiveness and generalizability of the proposed deep learning model with conductivity fields with different variances. A new connectivity indicator (connectivity ratio) is proposed to describe the connectivity area of a field. The network trained by conductivity fields with larger variances (higher heterogeneity) produces better PC identification and has a stronger generalizability. A conductivity field with a large variance usually has small connected areas and induces tortuous and long PC. The small connected areas restrict the generation of multiple PCs. This makes the model learn discriminative features easily. And the long PC increase its length, which means an increasing of the number of positive pixels in training samples. Finally, we compare the model with the stream function and minimum resistance methods. The results show that the deep learning model is an efficient method and can provide more connectivity information than the other two methods.