用神经网络估算雷达探测的地层参数，更准且能评估假设影响
Neural Posterior Estimation of Terrain Parameters from Radar Sounder Data
- 用GPU模拟器生成数据训练神经密度估计器，实现后验推断
- 在火星真实数据上验证，参数估计与文献值一致
- 可评估表面假设变化对结果的影响，适合行星科学应用
雷达声呐是通过处理发射雷达波的回波来探测地球及其他天体地下结构的电磁仪器。传统分析方法依赖近似假设，常产生忽略参数相关性及银河与测量噪声的点估计。本文提出一种基于仿真的推断方法，从雷达声呐数据中反演地层参数：利用基于GPU的模拟器生成合成观测数据，训练神经网络密度估计器进行神经后验估计（NPE）。通过显式条件化于参考表面假设，该框架可系统评估后验对参考表面变化的鲁棒性。我们在模拟数据上验证了模型的良好校准性，并证明其可迁移至真实火星雷达剖面，在文献指导的参考值下分析地层参数。
原文摘要 · Abstract (English)
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 train a neural network-based density estimator for neural posterior estimation (NPE). By explicitly conditioning on reference surface assumptions, the proposed framework allows systematic evaluation of posterior robustness to reference surface variability. We demonstrate that our NPE model is well calibrated on simulated data and transferable to real Mars radar profiles, where we analyze terrain parameters using literature-informed reference values.
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