Automated Methods for Detecting Volcanic Deformation Using Sentinel‐1 InSAR Time Series Illustrated by the 2017–2018 Unrest at Agung, Indonesia. Issue 2 (15th February 2020)

• Record Type: Journal Article
• Authors: Albino, F.; Biggs, J.; Yu, C.; Li, Z.
• Is Part Of: Journal of geophysical research. Volume 125:Issue 2(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-02-15
• Subjects: Sentinel‐1 time series -- atmospheric corrections -- weather model -- volcanic unrest -- real‐time monitoring -- Agung volcano; Geomagnetism -- Periodicals; Geochemistry -- Periodicals; Geophysics -- Periodicals; Earth sciences -- Periodicals; 551.1
• DOI: 10.1029/2019JB017908
• Languages: English
• ISSNs: 2169-9313

Abstract

Abstract: 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. Key Points: The use of high‐resolution weather models is crucial for correcting atmospheric signals in Sentinel‐1 interferograms at tropical volcanoes Sequential anomaly methods on corrected time series flag uplift at Agung with 95% confidence 1 month before the eruption Our study contributes to the development of tools suitable for real‐time monitoring of volcanic unrest in remote areas