Automated Methods for Detecting Volcanic Deformation Using Sentinel-1 InSAR Time Series Illustrated by the 2017-2018 Unrest at Agung, Indonesia
Automated Methods for Detecting Volcanic Deformation Using Sentinel-1 InSAR Time Series Illustrated by the 2017-2018 Unrest at Agung, Indonesia
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DOI：
10.1029/2019jb017908
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发表时间：
2020-02-01
影响因子：
3.9
通讯作者：
Li, Z.
中科院分区：
文献类型：
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作者：
Albino, F.;Biggs, J.;Li, Z.
Sentinel-1等雷达卫星现在能够产生世界各地任何火山的地面变形时间序列，但大气影响仍然限制了对热带火山动荡的实时探测。在这里，我们测试两种方法来纠正大气误差相位高程相关性和全球天气模型，并评估干涉合成孔径雷达（干涉合成孔径雷达）的时间序列检测变形异常的能力，使用固定的阈值或累积和控制图。我们使用2017-2018年阿贡火山危机作为案例，因为强烈的大气信号最初被误认为是真正的变形，并掩盖了与岩浆活动相关的微妙变形模式。我们评估了每种方法的受试者工作特征（ROC），发现ROC曲线下的平均面积为未经校正的数据（对应于无歧视能力）约0.5，在结合大气校正（天气模型和相位高程方法）后约为0.8，使用累积和控制图（其中1对应于类之间的理想分离）超过0.95。我们的研究结果回顾性地表明，到2017年10月，即地震群开始后15天和喷发前1个月，上升和下降时间序列的抬升可以被检测到95%的置信水平。因此，我们的方法成功地标记了异常行为，而不依赖于视觉检查或选择任意阈值，因此显示出作为全球火山观测站监测工具的潜力。
Radar satellites, such as Sentinel-1, are now able to produce time series of ground deformation at any volcano around the world, but atmospheric effects still limit the real-time detection of unrest at tropical volcanoes. Here, we test two approaches to correct atmospheric errors-phase elevation correlations and global weather models and assess the ability of Interferometric Synthetic Aperture Radar (InSAR) time series to detect deformation anomalies using either a fixed threshold or a cumulative sum control chart. We use the 2017-2018 crisis at Agung volcano as a case example because strong atmospheric signals were originally misidentified as true deformation, and obscured the subtle deformation pattern associated with magmatic activity. We assess the Receiver Operating Characteristics (ROC) of each method and found the average area under the ROC curve to be about 0.5 for the uncorrected data (corresponding to no discrimination capability), around 0.8 after combined atmospheric corrections (weather model and phase elevation approaches), and more than 0.95 using a cumulative sum control chart (where 1 corresponds to ideal separation between classes). Our results retrospectively show that uplift could have been detected to a 95% level of confidence for both ascending and descending time series by October 2017, 15 days after the start of the seismic swarm and 1 month prior to the eruption. Thus, our approach successfully flags anomalous behavior without relying on visual inspection or selection of an arbitrary threshold, and hence shows potential as a monitoring tool for volcano observatories globally.