5:15 PM - 6:45 PM
[SCG48-P06] Slip distribution of the Boso slow slip events using ocean bottom pressure gauges
Keywords:oceanic model, ocean bottom pressure gauge, Multi-channel Singular Spectrum Analysis, slow slip
1. Introduction
Slow slip events (SSEs) are slow fault slips without seismic wave radiation, and have been detected since GNSS observations. It is important for understanding the process of stress accumulation and release at plate boundaries to investigate SSEs. Most of SSEs occur under the sea area, then continuous observation of vertical movements using ocean bottom pressure gauges (OBPs) is useful. The OBP data include components such as oceanic tide, oceanic variation, and instrumental drift. To detect crustal movements, these components must be removed. Shimizu et al. (JpGU, 2021), Sato et al. (SSJ, 2022) developed a method which conducts the Multi-channel Singular Spectrum Analysis (MSSA) for the observed data and an oceanic model, and divides into components, and then, only good correlation components of the oceanic model are removed from the observed data. This method can extract crustal movement better, because this method may remove incompleteness of the oceanic model. This study estimates slip distribution of the Boso SSE using the OBP data.
2.Method
The oceanic model is MOVE/MRI.COM-JPN Dataset developed by MRI, JMA (Hirose, et al. 2020).
The OBP data are obtained at off Boso Peninsula from 2013-2015, and 2016-2018 which include the 2014 and 2018 Boso slow slip events.
From the observed data, we remove the oceanic tide using Baytap08 (Tamura et al. 1991), and remove the drift using a linear fitting. We conduct the MSSA for the removed data and the oceanic model data, and calculate correlation between them for each component. We reconstruct oceanic model data using only good correlation components, then remove them from the removed data. Then, to estimate crustal movements due to SSEs and seasonal deformation, we fit a parametric model to the residual between the removed data and the reconstructed oceanic model.
To estimate slip distribution of the SSE, we use the OBP data and daily F5 coordinate values of the GNSS stations provided by the Geospatial Information Authority of Japan. We remove drift and seasonal variation from the GNSS data, then we obtain the displacements due to the SSE. Using these data, we estimate slip distribution of the SSE by ABIC inversion with prior constraints. We use four prior constraints: the spatial roughness of slip, the temporal roughness of slip, the slip direction, and the deformation of the reference frame.
3.Results
The residual between the removed data and the reconstructed oceanic model is smaller than that between the removed data and the oceanic model itself. This suggests that the present method can remove oceanic variation better.
The estimation errors of the slip distribution with the OBP data are smaller than those with only the GNSS data. For the 2018 SSE, the slip distribution with the OBP data shows smaller and extends out to the sea compared to the slip distribution without the OBP data.
Acknowledgments
We thank the captains and crew of R/V Hakuho-maru, R/V Natsushima of JAMSTEC, R/V Kaiyo-maru No.3, R/V Kaiyo-maru No.5 of Kaiyo Engineering Co. LTD for their support. We thank Masahiro Suzuki, Chiba University, for providing the results in graduation thesis, Chiba University. We used daily F5 coordinate values by GIJ. This work was supported by the Earthquake and Volcano Hazards Observation and Research Program authorized by the Ministry of Education, Culture, Sports, Science and Technology. This work was supported by JSPS (25287109, 23K03541).
Slow slip events (SSEs) are slow fault slips without seismic wave radiation, and have been detected since GNSS observations. It is important for understanding the process of stress accumulation and release at plate boundaries to investigate SSEs. Most of SSEs occur under the sea area, then continuous observation of vertical movements using ocean bottom pressure gauges (OBPs) is useful. The OBP data include components such as oceanic tide, oceanic variation, and instrumental drift. To detect crustal movements, these components must be removed. Shimizu et al. (JpGU, 2021), Sato et al. (SSJ, 2022) developed a method which conducts the Multi-channel Singular Spectrum Analysis (MSSA) for the observed data and an oceanic model, and divides into components, and then, only good correlation components of the oceanic model are removed from the observed data. This method can extract crustal movement better, because this method may remove incompleteness of the oceanic model. This study estimates slip distribution of the Boso SSE using the OBP data.
2.Method
The oceanic model is MOVE/MRI.COM-JPN Dataset developed by MRI, JMA (Hirose, et al. 2020).
The OBP data are obtained at off Boso Peninsula from 2013-2015, and 2016-2018 which include the 2014 and 2018 Boso slow slip events.
From the observed data, we remove the oceanic tide using Baytap08 (Tamura et al. 1991), and remove the drift using a linear fitting. We conduct the MSSA for the removed data and the oceanic model data, and calculate correlation between them for each component. We reconstruct oceanic model data using only good correlation components, then remove them from the removed data. Then, to estimate crustal movements due to SSEs and seasonal deformation, we fit a parametric model to the residual between the removed data and the reconstructed oceanic model.
To estimate slip distribution of the SSE, we use the OBP data and daily F5 coordinate values of the GNSS stations provided by the Geospatial Information Authority of Japan. We remove drift and seasonal variation from the GNSS data, then we obtain the displacements due to the SSE. Using these data, we estimate slip distribution of the SSE by ABIC inversion with prior constraints. We use four prior constraints: the spatial roughness of slip, the temporal roughness of slip, the slip direction, and the deformation of the reference frame.
3.Results
The residual between the removed data and the reconstructed oceanic model is smaller than that between the removed data and the oceanic model itself. This suggests that the present method can remove oceanic variation better.
The estimation errors of the slip distribution with the OBP data are smaller than those with only the GNSS data. For the 2018 SSE, the slip distribution with the OBP data shows smaller and extends out to the sea compared to the slip distribution without the OBP data.
Acknowledgments
We thank the captains and crew of R/V Hakuho-maru, R/V Natsushima of JAMSTEC, R/V Kaiyo-maru No.3, R/V Kaiyo-maru No.5 of Kaiyo Engineering Co. LTD for their support. We thank Masahiro Suzuki, Chiba University, for providing the results in graduation thesis, Chiba University. We used daily F5 coordinate values by GIJ. This work was supported by the Earthquake and Volcano Hazards Observation and Research Program authorized by the Ministry of Education, Culture, Sports, Science and Technology. This work was supported by JSPS (25287109, 23K03541).