Abstract
Satellites enable widespread, regional or global surveillance of volcanoes and can provide the first indication of volcanic unrest or eruption. Here we consider Interferometric Synthetic Aperture Radar (InSAR), which can be employed to detect surface deformation with a strong statistical link to eruption. The ability of machine learning to automatically identify signals of interest in these large InSAR datasets has already been demonstrated, but data-driven techniques, such as convolutional neutral networks (CNN) require balanced training datasets of positive and negative signals to effectively differentiate between real deformation and noise. As only a small proportion of volcanoes are deforming and atmospheric noise is ubiquitous, the use of machine learning for detecting volcanic unrest is more challenging. In this paper, we address this problem using synthetic interferograms to train the AlexNet. The synthetic interferograms are composed of 3 parts: 1) deformation patterns based on a Monte Carlo selection of parameters for analytic forward models, 2) stratified atmospheric effects derived from weather models and 3) turbulent atmospheric effects based on statistical simulations of correlated noise. The AlexNet architecture trained with synthetic data outperforms that trained using real interferograms alone, based on classification accuracy and positive predictive value (PPV). However, the models used to generate the synthetic signals are a simplification of the natural processes, so we retrain the CNN with a combined dataset consisting of synthetic models and selected real examples, achieving a final PPV of 82%. Although applying atmospheric corrections to the entire dataset is computationally expensive, it is relatively simple to apply them to the small subset of positive results. This further improves the detection performance without a significant increase in computational burden.
Abstract (translated)
卫星能够对火山进行广泛、区域或全球的监测，并能提供火山动荡或爆发的第一个迹象。在这里，我们考虑干涉合成孔径雷达（insar），它可以用来探测表面变形，并具有很强的统计联系。机器学习自动识别这些大型InSAR数据集中感兴趣的信号的能力已经得到证明，但数据驱动技术，如卷积中性网络（CNN），需要平衡的正负信号训练数据集，以有效区分实际变形和噪声。由于只有一小部分火山变形，大气噪声无处不在，因此使用机器学习来检测火山动荡更具挑战性。在本文中，我们使用合成干涉图训练Alexnet来解决这个问题。合成干涉图由3部分组成：1）基于蒙特卡罗选择参数的变形模式，2）来自天气模型的分层大气效应，3）基于相关噪声统计模拟的湍流大气效应。基于分类精度和阳性预测值（ppv），用合成数据训练的Alexnet体系结构优于仅用真实干涉图训练的Alexnet体系结构。然而，用于生成合成信号的模型是对自然过程的简化，因此我们用一个由合成模型和选定的实际示例组成的组合数据集重新培训CNN，最终获得82%的ppv。尽管将大气校正应用于整个数据集的计算成本很高，但将其应用于正结果的小子集相对比较简单。这进一步提高了检测性能，而不会显著增加计算负担。
URL
https://arxiv.org/abs/1905.07286
