Multi-parameter Inversion of Tunnels Based on Deep Learning Constraints

Abstract

When tunnels traverse unfavorable geological formations, they often encounter the risk of sudden water-related incidents, posing a severe threat to the safety of construction workers and property. Consequently, there is an urgent demand for conducting research on the early detection of potential water-related disasters ahead of tunnel excavation. Among the various advanced exploration techniques, the DC resistivity method has proven to be more cost-effective and highly responsive to water-bearing structures, making it a prevalent choice for addressing sudden water hazards in real tunnel projects. This study introduces an innovative electrical approach underpinned by deep learning constraints, fusing resistivity and chargeability to pinpoint the location and size of underground water bodies. More specifically, we’ve designed a learning model rooted in the traditional U-Net convolutional network architecture, featuring a single encoder for feature extraction and a dual decoder for output. When compared to conventional linear inversion techniques and standalone parameter-based deep learning inversion methods, our proposed joint inversion approach exhibits superior performance.