scholarly journals Slow Slip Event On the Southern San Andreas Fault Triggered by the 2017 M w 8.2 Chiapas (Mexico) Earthquake

2019 ◽  
Vol 124 (9) ◽  
pp. 9956-9975 ◽  
Author(s):  
Ekaterina Tymofyeyeva ◽  
Yuri Fialko ◽  
Junle Jiang ◽  
Xiaohua Xu ◽  
David Sandwell ◽  
...  
Keyword(s):  
San Andreas Fault ◽  
Slow Slip ◽  
Slow Slip Event ◽  
San Andreas ◽  
Slip Event ◽  
Mexico Earthquake

scholarly journals Adjoint Slip Inversion under a Constrained Optimization Framework: Revisiting the 2006 Guerrero Slow Slip Event

2020 ◽  
Author(s):  
Josué Tago ◽  
Víctor M. Cruz-Atienza ◽  
Carlos Villafuerte ◽  
Takuya Nishimura ◽  
Vladimir Kostoglodov ◽  
...  
Keyword(s):  
Constrained Optimization ◽  
Slow Slip ◽  
Slow Slip Event ◽  
Optimization Framework ◽  
Slip Event

Observing seismic signatures of slow slip events with unsupervised learning

2021 ◽  
Author(s):  
Leonard Seydoux ◽  
Michel Campillo ◽  
René Steinmann ◽  
Randall Balestriero ◽  
Maarten de Hoop
Keyword(s):  
Clustering Algorithm ◽  
Low Frequency ◽  
Seismic Signal ◽  
Geodetic Data ◽  
Slow Slip ◽  
Slow Slip Events ◽  
Slow Slip Event ◽  
Intermittent Behavior ◽  
Slow Earthquakes ◽  
Slip Event

<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>

scholarly journals Seismicity at the Northern Hikurangi Margin, New Zealand, and Investigation of the Potential Spatial and Temporal Relationships With a Shallow Slow Slip Event

2019 ◽  
Vol 124 (5) ◽  
pp. 4751-4766 ◽  
Author(s):  
J. Yarce ◽  
A. F. Sheehan ◽  
J. S. Nakai ◽  
S. Y. Schwartz ◽  
K. Mochizuki ◽  
...  
Keyword(s):  
New Zealand ◽  
Slow Slip ◽  
Slow Slip Event ◽  
Temporal Relationships ◽  
Slip Event

scholarly journals Slow slip event within a gap between tremor and locked zones in the Nankai subduction zone

2016 ◽  
Vol 43 (3) ◽  
pp. 1066-1074 ◽  
Author(s):  
Ryota Takagi ◽  
Kazushige Obara ◽  
Takuto Maeda
Keyword(s):  
Subduction Zone ◽  
Slow Slip ◽  
Slow Slip Event ◽  
Slip Event ◽  
Nankai Subduction Zone

scholarly journals The 2010 slow slip event and secular motion at Kı̄lauea, Hawai‵i, inferred from TerraSAR‐X InSAR data

2014 ◽  
Vol 119 (8) ◽  
pp. 6667-6683 ◽  
Author(s):  
Jingyi Chen ◽  
Howard A. Zebker ◽  
Paul Segall ◽  
Asta Miklius
Keyword(s):  
Slow Slip ◽  
Secular Motion ◽  
Slow Slip Event ◽  
Slip Event
Sign in / Sign up

Export Citation Format

Share Document