Abstract The dielectric permittivity is a crucial parameter in planetary ground-penetrating radar (GPR) missions, such as the RIMFAX radar onboard the Mars 2020 Perseverance rover. It characterizes subsurface materials and enables depth interpretation of radargrams. In this study, we develop a deep learning–based approach for inverting dielectric permittivity from Radar Imager for Mars’ Subsurface Experiment (RIMFAX) GPR data. The architecture integrates a convolutional neural network, Bi-LSTM, and a self-attention mechanism, providing a principled framework for leveraging the sequential nature of GPR echoes, capturing long-range subsurface dependencies, and enhancing both the robustness and accuracy of inversions. The input is 1D processed GPR data, and the output is the corresponding 1D dielectric permittivity profile. By combining multiple 1D dielectric permittivity profiles, complex 2D profiles can be constructed. A large volume of synthetic data is used to train the model, allowing it to directly capture the intrinsic relationship between GPR data and dielectric permittivity. The approach is validated on the test set and then applied to the RIMFAX GPR data acquired by the Perseverance rover on Sols 389 and 770. The prediction results effectively reveal key characteristics of the subsurface sedimentary structure, including the number of layers, thicknesses, and the geometry of their contacts. It is broadly consistent with findings reported in prior research, demonstrating the great potential and promising applicability of the approach for dielectric permittivity inversion. However, in such complex planetary radar data, the true dielectric permittivity remains uncertain, and caution is therefore required when using permittivity estimates to infer subsurface structure.
Zhang et al. (Wed,) studied this question.