scholarly journals Imaging evolution of Cascadia slow-slip event using high-rate GPS

2021 ◽  
Author(s):  
Yuji Itoh ◽  
Yosuke Aoki ◽  
Junichi Fukuda

The slip history of short-term slow slip event (SSE) is typically inferred from daily Global Positioning System (GPS) data, which, however, cannot image the sub-daily processes, leaving the underlying mechanisms of SSEs elusive. To address the temporal resolution issue, we attempted to employ the kinematic subdaily GPS analysis, which has never been applied to SSE studies because its signal-to-noise ratio has been believed too low. By carefully post-processing sub-daily positions to remove non-tectonic position fluctuation, our 30-minute kinematic data clearly exhibits the transient motion of a few mm during one Cascadia SSE. A spatiotemporal slip image by inverting the 30-minute data exhibits a multi-stage evolution; it consists of an isotropic growth of SSE followed by an along-strike migration and termination within the rheologically controlled down-dip width. This transition at the slip growth mode is similar to the rupture growth of regular earthquakes, implying the presence of common mechanical factors behind the two distinct slip phenomena. The comparison with a slip inversion of the daily GPS demonstrates the current performance and limitation of the subdaily data in the SSE detection and imaging.Better understanding of the non-tectonic noise in the kinematic GPS analysis will further improve the temporal resolution of SSE.

2020 ◽  
Author(s):  
Josué Tago ◽  
Víctor M. Cruz-Atienza ◽  
Carlos Villafuerte ◽  
Takuya Nishimura ◽  
Vladimir Kostoglodov ◽  
...  

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>


2019 ◽  
Vol 124 (5) ◽  
pp. 4751-4766 ◽  
Author(s):  
J. Yarce ◽  
A. F. Sheehan ◽  
J. S. Nakai ◽  
S. Y. Schwartz ◽  
K. Mochizuki ◽  
...  

2016 ◽  
Vol 43 (3) ◽  
pp. 1066-1074 ◽  
Author(s):  
Ryota Takagi ◽  
Kazushige Obara ◽  
Takuto Maeda

2014 ◽  
Vol 119 (8) ◽  
pp. 6667-6683 ◽  
Author(s):  
Jingyi Chen ◽  
Howard A. Zebker ◽  
Paul Segall ◽  
Asta Miklius

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