Interferometric Synthetic Aperture Radar (InSAR) provides subcentimetric measurements of surface displacements, which are key for characterizing and monitoring magmatic processes in volcanic regions. The abundant measurements of surface displacements in multitemporal InSAR data routinely acquired by SAR satellites can facilitate near real‐time volcano monitoring on a global basis. However, the presence of atmospheric signals in interferograms complicates the interpretation of those InSAR measurements, which can even lead to a misinterpretation of InSAR signals and volcanic unrest. Given the vast quantities of SAR data available, an automatic InSAR data processing and denoising approach is required to separate volcanic signals that are cause of concern from atmospheric signals and noise. In this study, we employ a deep learning strategy that directly removes atmospheric and other noise signals from time‐consecutive unwrapped surface displacements obtained through an InSAR time series approach using an end‐to‐end convolutional neural network (CNN) with an encoder‐decoder architecture, modified U‐net. The CNN is trained with simulated synthetic unwrapped surface displacement maps and is then applied to real InSAR data. Our proposed architecture is capable of detecting dynamic spatio‐temporal patterns of volcanic surface displacements. We find that an ensemble‐average strategy is recommended to stabilize detected results for varying deformation rates and signal‐to‐noise ratios (SNRs). A case study is also presented where this method is applied to InSAR data covering Masaya volcano, Nicaragua and the results are validated using continuous GPS data. The results confirm that our network can indeed efficiently suppress atmospheric and other noise to reveal the noise‐free surface deformation.

中文翻译：

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使用深度学习自动检测火山表面变形

干涉合成孔径雷达（InSAR）提供了表面下位移的亚厘米级测量，这是表征和监测火山区岩浆过程的关键。SAR卫星常规获取的多时相InSAR数据中大量的地表位移测量值可以促进全球范围内近实时的火山监测。但是，干涉图中存在大气信号会使这些InSAR测量的解释变得复杂，甚至可能导致InSAR信号的误解和火山爆发。考虑到可用的大量SAR数据，需要一种自动的InSAR数据处理和降噪方法，以将引起关注的火山信号与大气信号和噪声分开。在这个研究中，我们采用了深度学习策略，该方法通过使用端到端卷积神经网络（CNN）和编码器-解码器架构，经修改的U编码器的InSAR时间序列方法，从时间连续的未包裹表面位移中直接去除大气和其他噪声信号-净。使用模拟的合成未包裹表面位移图训练CNN，然后将其应用于实际InSAR数据。我们提出的架构能够检测火山表面位移的动态时空模式。我们发现，建议采用整体平均策略来稳定检测结果，以适应不同的变形率和信噪比（SNR）。还介绍了一个案例研究，其中将该方法应用于覆盖尼加拉瓜马萨亚火山的InSAR数据，并使用连续GPS数据验证了结果。