Instantaneous inversion of transient electromagnetic data using machine learning.

Inverse problems are typically tackled using deterministic optimization methods that may become trapped in a local minimum or probabilistic methods that can be computationally demanding. In this study, we explore the potential of the back propagation neural network (BPNN) optimized by the genetic algorithm (GA) for onshore transient electromagnetic (TEM) inversion. The GA is employed to optimize the initial parameters of the BPNN, enhancing its global optimization ability. Once the BPNN optimized by GA (GA-BPNN) is properly trained, it can provide the distribution of subsurface electrical conductivity (σ) in 0.1 s. We train the GA-BPNN using synthetic datasets generated by TEM forward modeling and assess its reliability using both synthetic and field data. Theoretical simulations demonstrate that compared with BPNN, the error of GA-BPNN on the inversion results of six samples is reduced by 23.2%. Furthermore, this method can provide reliable results even in the presence of noise in the TEM response. Finally, applying this inversion method to karst exploration with measured data proves the reliability and robustness of the proposed method. The proposed method can support quasi-real-time imaging of subsurface structures and provides a powerful tool for the interpretation of field TEM data. [ABSTRACT FROM AUTHOR]