scholarly journals Slow slip rate and excitation efficiency of deep low-frequency tremors beneath southwest Japan

2018 ◽  
Vol 722 ◽  
pp. 314-323 ◽  
Author(s):  
Kumiko Daiku ◽  
Yoshihiro Hiramatsu ◽  
Takanori Matsuzawa ◽  
Tomoyuki Mizukami
Science ◽  
2010 ◽  
Vol 330 (6010) ◽  
pp. 1502-1502 ◽  
Author(s):  
Hitoshi Hirose ◽  
Youichi Asano ◽  
Kazushige Obara ◽  
Takeshi Kimura ◽  
Takanori Matsuzawa ◽  
...  

We identified a strong temporal correlation between three distinct types of slow earthquakes distributed over 100 kilometers along the dip of the subducting oceanic plate at the western margin of the Nankai megathrust rupture zone, southwest Japan. In 2003 and 2010, shallow very-low-frequency earthquakes near the Nankai trough as well as nonvolcanic tremor at depths of 30 to 40 kilometers were triggered by the acceleration of a long-term slow slip event in between. This correlation suggests that the slow slip might extend along-dip between the source areas of deeper and shallower slow earthquakes and thus could modulate the stress buildup on the adjacent megathrust rupture zone.


2017 ◽  
Vol 476 ◽  
pp. 122-131 ◽  
Author(s):  
O. Lengliné ◽  
W.B. Frank ◽  
D. Marsan ◽  
J.-P. Ampuero

2019 ◽  
Vol 219 (3) ◽  
pp. 2074-2096 ◽  
Author(s):  
Kazuro Hirahara ◽  
Kento Nishikiori

Summary A variety of slow slip events at subduction zones have been observed. They can be stress meters for monitoring the stress state of megathrust faults during their earthquake cycles. In this study, we focus on long-term slow slip events (LSSEs) recurring at downdip portions of megathrust faults among such slow earthquakes. Data analyses and simulation studies of LSSEs have so far been executed independently. In atmosphere and ocean sciences, data assimilations that optimally combine data analyses and simulation studies have been developed. We develop a method for estimating frictional properties and monitoring slip evolution on an LSSE fault, with a sequential data assimilation method, the ensemble Kalman filter (EnKF). We executed numerical twin experiments for the Bungo Channel LSSE fault in southwest Japan to validate the method. First, based on a rate- and state-dependent friction law, we set a rate-weakening circular LSSE patch on the rate-strengthening flat plate interface, whose critical nucleation size is larger than that of the patch, and reproduced the observed Bungo Channel LSSEs with recurrence times of approximately 7 yr and slip durations of 1 yr. Then, we synthesized the observed data of surface displacement rates at uniformly distributed stations with noises from the simulated slip model. Using our EnKF method, we successfully estimated the frictional parameters and the slip rate evolution after a few cycles. Secondly, we considered the effect of the megathrust fault existing in the updip portion of the LSSE fault, as revealed by kinematic inversion studies of Global Navigation Satellite System (GNSS) data and added this locked region with a slip deficit rate in the model. We estimated the slip rate on the locked region only kinematically, but the quasi-dynamic equation of motion in each LSSE fault cell includes the stress term arising from the locked region. Based on this model, we synthesized the observed surface displacement rate data for the actual distribution of GNSS stations and executed EnKF estimations including the slip rate on the locked region. The slip rate on the locked region could be quickly retrieved. Even for the actual distribution of GNSS stations, we could successfully estimate frictional parameters and slip evolution on the LSSE fault. Thus, our twin numerical experiments showed the validity of our EnKF method, although we need further studies for actual GNSS data analyses.


2021 ◽  
Author(s):  
Leonard Seydoux ◽  
Michel Campillo ◽  
René Steinmann ◽  
Randall Balestriero ◽  
Maarten de Hoop

<p>Slow slip events are observed in geodetic data, and are occasionally associated with seismic signatures such as slow earthquakes (low-frequency earthquakes, tectonic tremors). In particular, it was shown that swarms of slow earthquake can correlate with slow slip events occurrence, and allowed to reveal the intermittent behavior of several slow slip events. This observation was possible thanks to detailed analysis of slow earthquakes catalogs and continuous geodetic data, but in every case, was limited to particular classes of seismic signatures. In the present study, we propose to infer the classes of seismic signals that best correlate with the observed geodetic data, including the slow slip event. We use a scattering network (a neural network with wavelet filters) in order to find meaningful signal features, and apply a hierarchical clustering algorithm in order to infer classes of seismic signal. We then apply a regression algorithm in order to predict the geodetic data, including slow slip events, from the occurrence of inferred seismic classes. This allow to (1) identify seismic signatures associated with the slow slip events as well as (2) infer the the contribution of each classes to the overall displacement observed in the geodetic data. We illustrate our strategy by revisiting the slow-slip event of 2006 that occurred beneath Guerrero, Mexico.</p>


2020 ◽  
Vol 13 (10) ◽  
pp. 705-710
Author(s):  
Kyungjae Im ◽  
Demian Saffer ◽  
Chris Marone ◽  
Jean-Philippe Avouac

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