用深度学习方法提升雷达火山形变监测精度，显著降低大气干扰影响。
WaveDINO: Learning-Based Atmospheric Correction of Unwrapped InSAR Interferograms Validated by GNSS: Results at Laguna del Maule and Campi Flegrei Volcanoes
- 基于小波多尺度框架与预训练模型特征，融合真实大气噪声与合成形变数据训练。
- 在智利和意大利火山实测数据上，使地面站测量误差分别降低19%和3%。
- 适合地质监测、遥感反演等需要高精度形变分析的研究者使用。
干涉合成孔径雷达（InSAR）可有效监测火山形变，但观测信号常受大气相位延迟、季节性地表变化及去相关效应干扰。现有基于数值天气模型的方法虽能减弱干扰，但无法一致去除大气伪影，可能引入残留偏差。为此，我们提出一种新型学习型去噪方法WaveDINO，结合物理启发的合成形变与真实大气噪声，采用混合训练策略。该方法基于小波的多尺度去噪框架，以冻结的DINOv3基础模型特征和地形信息为条件，通过将合成岩浆源形变叠加于短期干涉图中，使网络暴露于真实大气统计特性的同时保留已知真值。在智利拉尼格拉·德尔·马乌莱和意大利坎皮弗莱格雷两个火山区域的长期真实干涉图上进行评估，并以独立的全球导航卫星系统（GNSS）测量作为验证。结果表明，WaveDINO持续优于对比模型，在两个站点上分别使平均GNSS偏差减少约3%和19%，并超越基于天气模型的校正方法。
原文摘要 · Abstract (English)
Interferometric Synthetic Aperture Radar (InSAR) enables effective monitoring of volcanic deformation; however, the observed signals are often corrupted by atmospheric phase delays, seasonal surface changes, and decorrelation effects. Existing atmospheric correction methods, such as numerical weather model-based methods, can reduce these effects but do not consistently remove atmospheric artefacts and may introduce residual biases. To address these limitations, we propose a novel learning-based method for denoising unwrapped InSAR interferograms, using a hybrid training strategy that combines physically motivated synthetic deformation with real atmospheric noise. Specifically, we introduce WaveDINO, a wavelet-based multi-scale denoising framework conditioned on frozen DINOv3 foundation-model features and terrain information. Training uses synthetic magma-source deformation superimposed on short-term interferograms to expose the network to realistic atmospheric statistics while retaining known ground truth. Performance is evaluated on both controlled synthetic data and long-term real interferograms from Laguna del Maule (Chile) and Campi Flegrei (Italy), with independent GNSS measurements used for validation. WaveDINO consistently outperforms competing models, improving agreement with GNSS measurements, and reducing mean GNSS misfit by approximately 3% and 19% at two sites, respectively, while surpassing weather-model-based corrections.
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