用物理规律指导扩散模型，精准填补地磁图空白
Physics-informed Diffusion Generation for Geomagnetic Map Interpolation
- 引入物理感知掩码，抑制噪声干扰
- 基于克里金原理约束生成结果，符合地磁物理规律
- 在4个真实数据集上表现优于现有方法
地磁图插值旨在推断空间点上的未观测地磁数据，在导航和资源勘探中具有关键应用。然而，现有散点数据插值方法未针对地磁图设计，易受探测噪声及物理规律影响，导致性能不佳。为此，本文提出物理感知扩散生成框架（PDG）以插补不完整地磁图。首先，设计基于局部感受野的物理感知掩码策略，引导扩散过程，有效消除噪声干扰；其次，依据地磁图的克里金原理对扩散生成结果施加物理约束，确保严格遵守物理规律。在四个真实数据集上的大量实验与深入分析表明，PDG各组件均具显著优势与有效性。
原文摘要 · Abstract (English)
Geomagnetic map interpolation aims to infer unobserved geomagnetic data at spatial points, yielding critical applications in navigation and resource exploration. However, existing methods for scattered data interpolation are not specifically designed for geomagnetic maps, which inevitably leads to suboptimal performance due to detection noise and the laws of physics. Therefore, we propose a Physics-informed Diffusion Generation framework~(PDG) to interpolate incomplete geomagnetic maps. First, we design a physics-informed mask strategy to guide the diffusion generation process based on a local receptive field, effectively eliminating noise interference. Second, we impose a physics-informed constraint on the diffusion generation results following the kriging principle of geomagnetic maps, ensuring strict adherence to the laws of physics. Extensive experiments and in-depth analyses on four real-world datasets demonstrate the superiority and effectiveness of each component of PDG.
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