基于物理的扩散生成用于地磁图插值
Physics-informed Diffusion Generation for Geomagnetic Map Interpolation
AI总结：
本文提出基于物理的扩散生成框架PDG，通过物理引导的掩码策略和约束，提升地磁图插值的精度与可靠性。
摘要：
地磁图插值旨在推断空间点上未观测到的地磁数据，对于导航和资源勘探具有关键应用。然而，现有散数据插值方法并未专门针对地磁图进行设计，这不可避免地导致性能低下，因为检测噪声和物理定律的存在。因此，我们提出了一种基于物理的扩散生成框架（PDG）来插值不完整的地磁图。首先，我们设计了一种基于局部感受野的物理引导掩码策略，有效消除噪声干扰。其次，我们对扩散生成结果施加基于地磁图克里格原理的物理约束，确保严格遵守物理定律。对四个真实世界数据集的广泛实验和深入分析证明了PDG各组件的优越性和有效性。
英文摘要：
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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