The uncertainty of subsurface resistivity can be determined using Bayesian inversion during electromagnetic exploration. However, the extensive sampling necessary for Bayesian inversion may result in high computational costs and prevent its application in complex large-scale 2D and 3D electromagnetic inversions. For the first attempt, we implement a 2.5D (i.e., 3D source with 2D geologic model) Bayesian inversion to analyze ground transient electromagnetics (TEM) and make some adaptations and improvements. To be specific, we use geostatistical-based random modeling as a prior constraint strategy and incorporate additional prior information through model mapping to improve its performance in adapting to complex terrains. To perform preinversion of prior parameters, we use a spatially constrained transdimensional Bayesian inversion approach, extracting priors for the formal inversion from the posterior of the preinversion. This not only reduces computation time but also lessens the formal Bayesian inversion’s reliance on prior selection. Furthermore, to obtain more realistic uncertainty assessments, we design a likelihood suited to the noise characteristics of TEM. The extensive statistical analysis of posterior results provides richer insights than previous methods, showing Bayesian inversion’s superiority in uncertainty reliability and resistivity recovery effectiveness over deterministic inversion and continuous 1D Bayesian inversion. Comparative studies with multiple simulated models and applications to complex terrain goaf areas validate the effectiveness and superiority of our algorithm.

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