Improved Real-Time Natural Hazard Monitoring Using Automated DInSAR Time Series
Improved Real-Time Natural Hazard Monitoring Using Automated DInSAR Time Series
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DOI：
10.3390/rs13050867
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发表时间：
2021-02
期刊：
影响因子：
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通讯作者：
K. Kelevitz;K. Tiampo;B. Corsa
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作者：
K. Kelevitz;K. Tiampo;B. Corsa
作为GeoSciFramework合作项目的一部分，我们正在为黄石火山地区建立一个监测系统，该系统将多个大地测量和地震数据集集成到一个先进的网络基础设施框架中，该框架将实现实时流数据分析和机器学习，并使我们能够更好地表征相关的长期和短期危害。目标是连续地摄取遥感（GNSS, DInSAR）和地面（地震，热和气体观测，应变计，倾斜仪和重力测量）数据，并近乎实时地查询和分析它们。在本研究中，我们重点研究了DInSAR数据的处理以及不同大气校正和实时轨道对自动处理和结果的影响。我们发现，欧洲中期天气预报中心（ECMWF）提供的大气校正目前是自动DInSAR处理的最佳选择，而实时轨道的使用足以用于所讨论的预警应用。我们在夏威夷基拉韦厄火山地区的一个测试案例中展示了对大气修正和使用实时轨道的分析。最后，利用这些发现，我们给出了黄石地区2018年5月至2019年10月的位移时间序列结果，该结果与GNSS数据非常吻合。这些结果将有助于建立一个基线模型，该模型将成为未来预警系统的基础，该系统将不断更新新的DInSAR数据。
As part of the collaborative GeoSciFramework project, we are establising a monitoring system for the Yellowstone volcanic area that integrates multiple geodetic and seismic data sets into an advanced cyber-infrastructure framework that will enable real-time streaming data analytics and machine learning and allow us to better characterize associated long- and short-term hazards. The goal is to continuously ingest both remote sensing (GNSS, DInSAR) and ground-based (seismic, thermal and gas observations, strainmeter, tiltmeter and gravity measurements) data and query and analyse them in near-real time. In this study, we focus on DInSAR data processing and the effects from using various atmospheric corrections and real-time orbits on the automated processing and results. We find that the atmospheric correction provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) is currently the most optimal for automated DInSAR processing and that the use of real-time orbits is sufficient for the early-warning application in question. We show analysis of atmospheric corrections and using real-time orbits in a test case over the Kilauea volcanic area in Hawaii. Finally, using these findings, we present results of displacement time series in the Yellowstone area between May 2018 and October 2019, which are in good agreement with GNSS data where available. These results will contribute to a baseline model that will be the basis of a future early-warning system that will be continuously updated with new DInSAR data acquisitions.