5:15 PM - 7:15 PM
[MTT37-P04] Reevaluating crustal deformation around Hakone volcano based on dense GNSS data
Keywords:Hakone volcano, crustal deformation, GNSS
At Hakone volcano, earthquake swarms have occurred approximately once every few years since 2001, and crustal deformation, which indicates inflation of volcanic edifice, has been repeatedly observed during these events. For example, the transient crustal deformation observed during the 2001 event was explained using a spherical pressure source and shallow open cracks (Daita et al., 2009, Kazan). In 2015, an earthquake swarm occurred around Hakone volcano, eventually leading to a small-scale phreatic eruption. The transient crustal deformation at that time was also explained using similar models (Harada et al., 2018 EPS; Honda et al., 2018 EPS; Kobayashi et al., 2018 EPS).
On the other hand, using dense GNSS data, Doke et al. (2024 JpGU) reported that eastward displacement in the Ashigara Plain preceded the inflation of Hakone volcano during the 2023 event. They suggested that the deformation could be explained by fault slip beneath the Ashigara Plain. Moreover, there are some past transient crustal deformations that the conventional model could not explain. Therefore, based on the findings from the 2023 event, it is necessary to reassess the existing models for understanding volcanic phenomena accurately. In this study, we analyze dense GNSS observation data to accurately separate and classify deformation caused by volcanic activity at Hakone Volcano from other types of deformation. The ultimate goal is to elucidate the tectonics in the Izu-Hakone region.
For the analysis, we used data from the Geospatial Information Authority of Japan’s (GSI) GEONET and GNSS sites operated independently by the Japan Meteorological Agency and the Hot Springs Research Institute of Kanagawa Prefecture. Additionally, we used GNSS sites operated by SoftBank the period after their network launched (2019). Using these datasets, we calculated the time series of baseline length variations to characterize crustal deformation around Hakone volcano and attempted to classify deformation types associated with transient events.
Variations of Baseline length among the GEONET observation sites Susono-2, Hakone, and Odawara show plural transient crustal deformation events. These events can be classified into three types: Type 1 is the pattern associated with a spherical pressure source and a shallow open crack (e.g., deformation in 2001 and 2015). Type 2 is the pattern associated only with a spherical pressure source (e.g., deformation in 2006). Type 3 is the pattern that could not be explained by conventional models (e.g., deformation in 2008–2009, 2012–2013, 2017, 2019, 2021, and 2023)
Type 1 may also include deformations that close the cracks after the volcanic unrest halted. Additionally, the regional tectonics around Hakone, like fault slips beneath the Ashigara Plain, might influence crustal deformation that a spherical pressure source cannot explain (Type 3).
In our presentation, we will show the results of model estimations for some events that represent each type.
GNSS data from the SoftBank observation network were provided by SoftBank Corp. and ALES Corp. to the “Consortium to utilize the SoftBank original reference sites for Earth and Space Science” under the associated contract. We used the daily coordinates of SoftBank sites, which were routinely analyzed by Tohoku University. We also used GNSS data from GSI and JMA.
On the other hand, using dense GNSS data, Doke et al. (2024 JpGU) reported that eastward displacement in the Ashigara Plain preceded the inflation of Hakone volcano during the 2023 event. They suggested that the deformation could be explained by fault slip beneath the Ashigara Plain. Moreover, there are some past transient crustal deformations that the conventional model could not explain. Therefore, based on the findings from the 2023 event, it is necessary to reassess the existing models for understanding volcanic phenomena accurately. In this study, we analyze dense GNSS observation data to accurately separate and classify deformation caused by volcanic activity at Hakone Volcano from other types of deformation. The ultimate goal is to elucidate the tectonics in the Izu-Hakone region.
For the analysis, we used data from the Geospatial Information Authority of Japan’s (GSI) GEONET and GNSS sites operated independently by the Japan Meteorological Agency and the Hot Springs Research Institute of Kanagawa Prefecture. Additionally, we used GNSS sites operated by SoftBank the period after their network launched (2019). Using these datasets, we calculated the time series of baseline length variations to characterize crustal deformation around Hakone volcano and attempted to classify deformation types associated with transient events.
Variations of Baseline length among the GEONET observation sites Susono-2, Hakone, and Odawara show plural transient crustal deformation events. These events can be classified into three types: Type 1 is the pattern associated with a spherical pressure source and a shallow open crack (e.g., deformation in 2001 and 2015). Type 2 is the pattern associated only with a spherical pressure source (e.g., deformation in 2006). Type 3 is the pattern that could not be explained by conventional models (e.g., deformation in 2008–2009, 2012–2013, 2017, 2019, 2021, and 2023)
Type 1 may also include deformations that close the cracks after the volcanic unrest halted. Additionally, the regional tectonics around Hakone, like fault slips beneath the Ashigara Plain, might influence crustal deformation that a spherical pressure source cannot explain (Type 3).
In our presentation, we will show the results of model estimations for some events that represent each type.
GNSS data from the SoftBank observation network were provided by SoftBank Corp. and ALES Corp. to the “Consortium to utilize the SoftBank original reference sites for Earth and Space Science” under the associated contract. We used the daily coordinates of SoftBank sites, which were routinely analyzed by Tohoku University. We also used GNSS data from GSI and JMA.