scholarly journals GRACE detects coseismic and postseismic deformation from the Sumatra-Andaman earthquake

2007 ◽  
Vol 34 (13) ◽  
pp. n/a-n/a ◽  
Author(s):  
J. L. Chen ◽  
C. R. Wilson ◽  
B. D. Tapley ◽  
S. Grand
Keyword(s):  
Postseismic Deformation ◽  
Andaman Earthquake

scholarly journals Upper mantle rheology from GRACE and GPS postseismic deformation after the 2004 Sumatra-Andaman earthquake

2010 ◽  
Vol 11 (6) ◽  
pp. n/a-n/a ◽  
Author(s):  
I. Panet ◽  
F. Pollitz ◽  
V. Mikhailov ◽  
M. Diament ◽  
P. Banerjee ◽  
...  
Keyword(s):  
Upper Mantle ◽  
Postseismic Deformation ◽  
Mantle Rheology ◽  
Andaman Earthquake

No evidence of unusually large postseismic deformation in Andaman region immediately after 2004 Sumatra-Andaman earthquake

2008 ◽  
Vol 35 (10) ◽  
Author(s):  
V. K. Gahalaut ◽  
J. K. Catherine ◽  
Sridevi Jade ◽  
R. Gireesh ◽  
D. C. Gupta ◽  
...  
Keyword(s):  
Postseismic Deformation ◽  
Andaman Earthquake

A comprehensive model of postseismic deformation of the 2004 Sumatra–Andaman earthquake deduced from GPS observations in northern Sumatra

2014 ◽  
Vol 88 ◽  
pp. 218-229 ◽  
Author(s):  
Endra Gunawan ◽  
Takeshi Sagiya ◽  
Takeo Ito ◽  
Fumiaki Kimata ◽  
Takao Tabei ◽  
...  
Keyword(s):  
Postseismic Deformation ◽  
Comprehensive Model ◽  
Andaman Earthquake ◽  
Gps Observations

scholarly journals Adjoint-based direct data assimilation of GNSS time series for optimizing frictional parameters and predicting postseismic deformation following the 2003 Tokachi-oki earthquake

2020 ◽  
Vol 72 (1) ◽  
Author(s):  
Masayuki Kano ◽  
Shin’ichi Miyazaki ◽  
Yoichi Ishikawa ◽  
Kazuro Hirahara
Keyword(s):  
Time Series ◽  
Data Assimilation ◽  
Evaluation Method ◽  
Temporal Evolution ◽  
Recovery Process ◽  
Postseismic Deformation ◽  
Coseismic Slip ◽  
Megathrust Earthquakes ◽  
Spatio Temporal ◽  
Gnss Time Series

Abstract Postseismic Global Navigation Satellite System (GNSS) time series followed by megathrust earthquakes can be interpreted as a result of afterslip on the plate interface, especially in its early phase. Afterslip is a stress release process accumulated by adjacent coseismic slip and can be considered a recovery process for future events during earthquake cycles. Spatio-temporal evolution of afterslip often triggers subsequent earthquakes through stress perturbation. Therefore, it is important to quantitatively capture the spatio-temporal evolution of afterslip and related postseismic crustal deformation and to predict their future evolution with a physics-based simulation. We developed an adjoint data assimilation method, which directly assimilates GNSS time series into a physics-based model to optimize the frictional parameters that control the slip behavior on the fault. The developed method was validated with synthetic data. Through the optimization of frictional parameters, the spatial distributions of afterslip could roughly (but not in detail) be reproduced if the observation noise was included. The optimization of frictional parameters reproduced not only the postseismic displacements used for the assimilation, but also improved the prediction skill of the following time series. Then, we applied the developed method to the observed GNSS time series for the first 15 days following the 2003 Tokachi-oki earthquake. The frictional parameters in the afterslip regions were optimized to A–B ~ O(10 kPa), A ~ O(100 kPa), and L ~ O(10 mm). A large afterslip is inferred on the shallower side of the coseismic slip area. The optimized frictional parameters quantitatively predicted the postseismic GNSS time series for the following 15 days. These characteristics can also be detected if the simulation variables can be simultaneously optimized. The developed data assimilation method, which can be directly applied to GNSS time series following megathrust earthquakes, is an effective quantitative evaluation method for assessing risks of subsequent earthquakes and for monitoring the recovery process of megathrust earthquakes.

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