Joint Inversion of UMRS-TEM Data and Its Application for Detection in the Tunnel Using Hamiltonian Monte Carlo Method

Underground magnetic resonance sounding (UMRS) can effectively explore the aquifer information in front of the tunnel face. However, the complex tunnel construction environment will reduce the reliability of the interpretation results. We propose and implement using joint inversion of UMRS-transient electromagnetic (TEM) data in tunnel detection using Hamiltonian Monte Carlo (HMC) for the first time to solve this problem. Joint inversion can update the UMRS kernel in real time during the iterative process, obtain accurate aquifer information and resistivity structure, and improve the reliability of the interpretation of inversion. HMC is a Markov chains Monte Carlo (MCMC) method that uses Hamiltonian dynamics to propose future states in Markov chains. It can explore the target distribution more effectively, and it also has the advantage that MCMC can obtain a posteriori probability density function (pdf) of parameters, and we believe that pdf information can effectively judge the accuracy of inversion results. We validated the effectiveness and practicality of the joint inversion using synthetic and observed data, and the experimental results showed its advantages in accuracy and noise resistance compared to a single UMRS inversion. In the field example, the water content pdf of the joint inversion is significantly increased by 31.8% compared with that of the single inversion. We analyzed the correlation between water content, resistivity, and layer interfaces and discovered some correlation laws. HMC improves the efficiency of computational and provides assistance for further research on the influence of parameters on inversion results. Our conclusions can improve the safety of tunnel construction and provide effective technical support for avoiding hydrogeological disasters in tunnels.