Inverse Modeling of Airborne Time-Domain Electromagnetic (ATEM) Data

Time-domain airborne electromagnetic (ATEM) methods have gained a prominent role in mineral exploration, hydrogeological investigations, and environmental monitoring due to their rapid data acquisition, extensive spatial coverage, and capability to probe considerable depths. Despite these advantages, the quantitative interpretation of ATEM data remains challenging because of the inherent ill-posedness of electromagnetic inversion, the non-uniqueness of recovered models, sensitivity to noise, and the influence of systematic calibration uncertainties. These limitations necessitate careful implementation of forward modeling and inversion strategies, along with appropriate regularization techniques.

In this study, the ATEM system response was investigated based on the fundamental principles of electromagnetism and established analytical formulations for one-dimensional (1D) layered-earth models. Both frequency-domain and time-domain representations were examined to provide a comprehensive understanding of the electromagnetic response behavior. Using widely accepted computational tools for forward simulation and inversion, the performance and reliability of these frameworks were evaluated through both synthetic modeling and real-data applications.

Forward modeling conducted on synthetic layered-earth scenarios demonstrated that variations in electrical conductivity and layer thickness exert a strong control on the amplitude and temporal decay characteristics of the secondary electromagnetic fields. Specifically, shallow conductive layers generate high-amplitude responses with rapid decay rates, while deeper or more resistive structures produce lower-amplitude signals that persist over longer time intervals. These results highlight the sensitivity of ATEM data to subsurface electrical properties and confirm the theoretical expectations derived from diffusion-based electromagnetic theory. Subsequent inversion of the synthetic datasets showed that the general resistivity structure of the subsurface can be recovered with acceptable accuracy, although fine-scale features may be smoothed due to regularization and limited resolution. The inversion results emphasize the trade-off between data fit and model smoothness, which is a critical aspect of stabilizing ill-posed geophysical problems.

In the applied stage, real ATEM data acquired from multiple survey profiles in Australia were analyzed to assess the practical performance of the methodology. The inverted resistivity sections revealed significant geological features, including deep conductive zones, near-surface resistive layers, and structural discontinuities associated with faults and fracture systems.

These interpretations are consistent with available geological and geophysical information, demonstrating the reliability and effectiveness of the adopted approach. Overall, the findings of this study indicate that the careful application of standard forward modeling and inversion algorithms enables realistic estimation of subsurface geo-electrical structures from ATEM data. The method proves to be highly valuable for mineral exploration, groundwater assessment, and environmental studies. To further enhance the robustness and resolution of ATEM interpretations, future work should focus on incorporating prior geological constraints, integrating complementary geophysical datasets such as magnetic and seismic data, and advancing toward three-dimensional inversion frameworks. Additionally, systematic uncertainty quantification using stochastic or resampling techniques is recommended to better assess model reliability and reduce ambiguity in interpretation.