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.

中文翻译：

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使用自动DInSAR时间序列改进的实时自然灾害监测

作为协作性GeoSciFramework项目的一部分，我们正在建立黄石火山区的监控系统，该系统将多个大地测量和地震数据集集成到高级的网络基础架构中，从而可以进行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。目标是连续摄取遥感（GNSS，DInSAR）和地面（地震，热和气体观测，应变仪，倾角仪和重力测量）数据，并以近实时方式查询和分析它们。在这项研究中，我们专注于DInSAR数据处理以及使用各种大气校正和实时轨道对自动处理和结果的影响。我们发现，欧洲中距离天气预报中心（ECMWF）提供的大气校正目前对于自动DInSAR处理而言是最理想的，而实时轨道的使用足以解决上述预警应用。我们在夏威夷基拉韦厄火山地区的一个测试案例中显示了大气校正分析和使用实时轨道的分析。最后，利用这些发现，我们介绍了黄石地区2018年5月至2019年10月之间位移时间序列的结果，这些结果与可用的GNSS数据高度吻合。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，该预警系统将随着新的DInSAR数据采集而不断更新。