Neural Networks Estimate Terrain Parameters from Radar Data

The paper presents a simulation-based inference approach to terrain parameter inversion from radar sounder data, utilizing neural posterior estimation (NPE). Conventional methods often rely on approximate assumptions and produce point estimates that ignore parameter correlations and noise. The proposed approach addresses these limitations by training a neural network-based density estimator on synthetic observations generated by a GPU-based simulator. This allows for a more accurate and robust estimation of terrain parameters. A key feature of the framework is its ability to systematically evaluate posterior robustness to reference surface variability. The NPE model demonstrates good calibration on simulated data and is transferable to real Mars radar profiles, enabling the analysis of terrain parameters using literature-informed reference values. This research has significant implications for planetary science, as it provides a more accurate and efficient method for analyzing radar sounder data and mapping subsurface environments. The use of neural networks and simulation-based inference represents a significant advancement in the field. However, the accuracy of the model is dependent on the quality and representativeness of the simulated data.

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