Abstract
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.
Highlights
Natural catastrophes occur at a variety of spatial and temporal scales
We explore the use of atmospheric corrections from two providers: European Centre for Medium-Range Weather Forecasts
Area: no atmospheric correction applied, European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric correction applied [33], and Generic Atmospheric Correction Online Service (GACOS) atmospheric correction applied [34]. We find that both atmospheric corrections help remove apparent uplift signal at the highest elevations of the island and keep the subsidence signal close to the southeast shoreline that is related to the 2018 Kilauea eruptions
Summary
Natural catastrophes occur at a variety of spatial and temporal scales. In particular, solid earth hazards, such as large earthquakes and volcanic eruptions, often have very long inter-event times and this makes it difficult to forecast their behavior. We monitor a significant number of active volcanoes around the world, collecting geodetic data in the form of GNSS and SAR data, in conjunction with ground-based measurements from strainmeters, tiltmeters and gravity instruments with the goal of providing early warning and reducing risk and losses. Natural volcano observatories that collect large volumes of variable data over long time periods provide a unique opportunity to develop a framework that integrates modern data sources and advanced computational algorithms. This is essential to both early detection of volcanic activity and forecasting eruptions on intermediate- and short-term time scales
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