arXiv:2605.08179v2 Announce Type: replace-cross Abstract: Radar sounders are electromagnetic instruments that can probe deep into the subsurface of Earth and other planetary bodies by processing the echo of transmitted radar waves. Conventional approaches for analyzing such data rely on approximate assumptions and often produce point estimates that ignore parameter correlations as well as galactic and measurement noise. We propose a simulation-based inference approach to terrain parameter inversion from radar sounder data, where synthetic observations from a GPU-based simulator are used to tra
Advances in AI, particularly simulation-based inference within machine learning, are enabling more sophisticated analysis of complex scientific data.
Improved detection and analysis capabilities in radar sounders have applications in planetary science and potentially in resource exploration and defence, contributing to a better understanding of subsurface environments.
The ability to accurately interpret radar sounder data will move from approximate assumptions to more precise, noise-aware, and correlation-considering parameter estimations, offering a clearer subsurface picture.
- · Planetary science researchers
- · Space exploration agencies
- · GPU manufacturers
- · Machine learning researchers
- · Legacy data analysis methods
- · Subsurface mapping firms relying on less precise techniques
More accurate subsurface maps of extraterrestrial bodies will be generated, improving target selection for missions.
Enhanced subsurface data could inform resource prospecting on other planets or even improve terrestrial geological surveys.
The methodology could be adapted to other forms of remote sensing and geophysical analysis, broadening AI's application in Earth and space sciences.
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Read at arXiv cs.LG