Surface deformation of Asama volcano, Japan, detected by time series InSAR combining persistent and distributed scatterers, 2014‒2018

Abstract

Asama volcano is one of the most active volcanoes in Japan. Spatially dense surface deformation at Asama volcano has rarely been documented because of its high topography and snow cover around the summit. This study presents the first interferometric synthetic aperture radar (InSAR) observation of ground deformation at Asama volcano with 120 Sentinel-1 SAR images from both ascending and descending tracks and 20 descending ALOS-2 images acquired between 2014 and 2018. We exploited both persistent and distributed scatterers to overcome decorrelation of SAR signals and applied a three-dimensional unwrapping method to retrieve the displacement time series efficiently. Our observations reveal an asymmetric deformation around the volcano with two main deformation regions on the northeast and southeast flanks, respectively. The northeast flank (NEF) exhibits line-of-sight (LOS) extensions in all the three SAR datasets with maximum velocities of − 14, − 10, and − 12 mm/year for the descending ALOS-2, ascending, and descending Sentinel-1 measurements, respectively. The southeast flank (SEF) shows LOS extensions in the ascending observations and LOS shortening in the descending observations with velocities between − 12 and 9 mm/ year. Decomposition of the LOS displacements reveals nearly pure subsidence at the NEF, while the SEF exhibits a substantial eastward component as well as subsidence. Comparisons of the vertical subsidence at two continuous GNSS stations near the summit crater with our InSAR observations indicate small discrepancies smaller than 4 mm/year. We interpreted that the subsidence at the NEF of Asama is primarily due to the hydrothermal activity, while the deformation at SEF is plausibly due to flank instability. We highlight that efforts should be taken to monitor the slope instability at Asama volcano in the future.

LiCSBAS: An Open-Source InSAR Time Series Analysis Package Integrated with the LiCSAR Automated Sentinel-1 InSAR Processor

“…This step is optional but recommended to improve the accuracy of the time series (see Section 3.4). However, since the GACOS corrections may have a negative impact in some cases [46,47], users should check if reasonable noise mitigation has been achieved after applying the corrections. LiCSBAS calculates the STD of unwrapped phases before and after the GACOS corrections and their reduction rates for each interferogram, as well as creates quick-look images of unwrapped interferograms before and after the correction and the correction itself to aid in checking the GACOS effects ( Figure S1).…”

Observing Posteruptive Deflation of Hydrothermal System Using InSAR Time Series Analysis: An Application of ALOS‐2/PALSAR‐2 Data on the 2015 Phreatic Eruption of Hakone Volcano, Japan

“…So what is the cause of the posteruptive deflation in Hakone volcano? One possible cause of posteruptive deflation is compaction due to a decrease in pore pressure (Todesco et al., 2014; Wang et al., 2019). Because the behavior of crustal deformations during the 2015 phreatic eruption suggests fluid migration from the hydrothermal reservoir to a shallower edifice (Doke et al., 2018), the preeruptive pore pressure could have been released during and after the migration (Figure 4).…”

“…With the recent development of synthetic aperture radar (SAR) technology, posteruptive deflation has been observed after phreatic eruptions in various volcanoes (e.g., Hamling et al., 2016; Himematsu et al., 2020; Narita & Murakami, 2018). Volcanic deflation, which occurs at different temporal and spatial scales, is explained by various factors, such as decreases in pore pressure resulting from fluid migration (e.g., Todesco et al., 2014; Wang et al., 2019) and thermoelastic responses with cooling (e.g., Furuya, 2005; Wang & Aoki, 2019). Constraining the source of posteruptive deflation is important when evaluating the structure and physical properties of hydrothermal systems beneath volcanoes and assessing the risk of future phreatic eruptions and signals during volcanic unrest.…”

Mapping surface displacement using a pair of interferograms: comparative study

“…For examples of use of this approach see [Joughin et al, 1998]; ; [Bell et al, 2008]; [Samieie-Esfahany et al, 2009]; [Kumar et al, 2011]; [Ozawa and Ueda, 2011]; [Wang et al, 2012]; [Rucci et al, 2013]; [Chaussard et al, 2013]; [Chaussard et al, 2014]; [Samsonov et al, 2014], [Chaussard, 2016]; [Dumont et al, 2016]; [Hutchison et al, 2016]; [Parker et al, 2016]; [Wittmann et al, 2017], [Drouin and Sigmundsson, 2019] and [Wang et al, 2019]. However, such assumption necessarily affects the inferred value of the two remaining components considering the total displacement detected in the LOS is still the same.…”