scholarly journals Sensitivity of tidal marshes as recorders of major megathrust earthquakes: constraints from the 25 December 2016 M w 7.6 Chiloé earthquake, Chile

Author(s):  
Martin Brader ◽  
Ed Garrett ◽  
Daniel Melnick ◽  
Ian Shennan
2019 ◽  
Author(s):  
Marino Protti ◽  
◽  
Nathan Bangs ◽  
Peter Baumgartner ◽  
Donald Fisher ◽  
...  

2021 ◽  
Vol 261 ◽  
pp. 106922
Author(s):  
Alan R. Nelson ◽  
Christopher B. DuRoss ◽  
Robert C. Witter ◽  
Harvey M. Kelsey ◽  
Simon E. Engelhart ◽  
...  

2020 ◽  
Vol 72 (1) ◽  
Author(s):  
Masayuki Kano ◽  
Shin’ichi Miyazaki ◽  
Yoichi Ishikawa ◽  
Kazuro Hirahara

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.


2020 ◽  
Vol 36 (3) ◽  
pp. 1271-1297
Author(s):  
Kenneth W. Campbell

In this article, I propose a method for estimating the magnitude [Formula: see text] at which subduction megathrust earthquakes are expected to exhibit a break in magnitude scaling of both seismic source dimensions and earthquake ground motions. The methodology is demonstrated by applying it to 79 global subduction zones defined in the literature, including Cascadia. Breakpoint magnitude is estimated from seismogenic interface widths, empirical source scaling relations, and aspect ratios of physically unbounded earthquake ruptures and their uncertainties. The concept stems from the well-established observation that source-dimension and ground motion scaling decreases for shallow continental (primarily strike-slip) earthquakes when rupture exceeds the seismogenic width of the fault. Although a scaling break for megathrust earthquakes is difficult to observe empirically, all of the instrumentally recorded historical [Formula: see text] mega-earthquakes have occurred on subduction zones with [Formula: see text] (8.1–8.9), consistent with an observed break in source scaling relations derived from these same events. The breakpoint magnitudes derived in this study can be used to constrain the magnitude at which the scaling of ground motion is expected to decrease in subduction ground motion prediction equations.


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