scholarly journals The state of stress on some faults of the San Andreas System as inferred from near-field strong motion data

1997 ◽  
Vol 102 (B6) ◽  
pp. 11731-11744 ◽  
Author(s):  
Michel Bouchon
Keyword(s):  
Near Field ◽  
Strong Motion ◽  
The State ◽  
Strong Motion Data ◽  
Motion Data ◽  
San Andreas ◽  
State Of Stress

scholarly journals Rupture history of the 1997 Umbria-Marche (Central Italy) main shocks from the inversion of GPS, DInSAR and near field strong motion data

2009 ◽  
Vol 47 (4) ◽  
Author(s):  
B. Hernandez ◽  
M. Cocco ◽  
F. Cotton ◽  
S. Stramondo ◽  
O. Scotti ◽  
...  
Keyword(s):  
Near Field ◽  
Central Italy ◽  
Strong Motion ◽  
Strong Motion Data ◽  
Motion Data ◽  
Main Shocks ◽  
History Of

Loreto Intermediate Depth Earthquake of 26 May 2019 (Northeast Peru): Source Parameters by Inversion of Local to Regional Waveforms and Strong-Motion Observations

2021 ◽  
Author(s):  
Hernando Tavera ◽  
Bertrand Delouis ◽  
Arturo Mercado ◽  
David Portugal
Keyword(s):  
Near Field ◽  
Source Parameters ◽  
Waveform Inversion ◽  
Strong Motion ◽  
Seismic Source ◽  
Slab Geometry ◽  
Nazca Plate ◽  
Strong Motion Data ◽  
Ground Shaking ◽  
Motion Data

Abstract The Loreto earthquake of 26 May 2019 occurred below the extreme northeast part of Peru at a depth of 140 km within the subducting Nazca plate at a distance of 700 km from the trench Peru–Chile. The orientation of the seismic source was obtained from waveform inversion in the near field using velocity and strong-motion data. The rupture occurred in normal faulting corresponding to a tensional process with T axis oriented in east–west direction similar to the direction of convergence between the Nazca and South America plates. The analysis of the strong-motion data shows that the levels of ground shaking are very heterogeneous with values greater than 50 Gal up to distances of 300 km; the maximum recorded acceleration of 122 Gal at a distance of 100 km from the epicenter. The Loreto earthquake is classified as a large extensional event in the descending Nazca slab in the transition from flat-slab geometry to greater dip.

scholarly journals Source process and slip model of 2005 Dahuiyeh-Zarand earthquake (Iran) using inversion of near-field strong motion data

2012 ◽  
Vol 189 (1) ◽  
pp. 669-680 ◽  
Author(s):  
R. Rouhollahi ◽  
M. R. Ghayamghamian ◽  
F. Yaminifard ◽  
P. Suhadolc ◽  
M. Tatar
Keyword(s):  
Near Field ◽  
Strong Motion ◽  
Source Process ◽  
Slip Model ◽  
Strong Motion Data ◽  
Motion Data

First strong-motion instrumentation in Hawaii: With results from the earthquake of April 26, 1973

1974 ◽  
Vol 64 (6) ◽  
pp. 1909-1918
Author(s):  
W. M. Adams
Keyword(s):  
Strong Motion ◽  
The State ◽  
Strong Motion Data ◽  
Motion Data ◽  
Extensive Damage

abstract On April 26, 1973, an earthquake with magnitude of 614 occurred at a depth of 40 km below Honomu (north of Hilo). Serendipitously, in the preceding February, one SMA-1 accelerograph and one Wilmot seismoscope were installed near Waikiki on the island of Oahu. Also, one SMA-1 accelerograph and three Wilmot seismoscopes were installed on the island of Hawaii. The records from these instruments provided the first strong-motion data for the State of Hawaii. The motion was dominantly horizontal. This finding explains the extensive damage to the Hilo area, reported to be about 6 million dollars.

scholarly journals Near-field strong-motion data from nuclear explosive sources

1984 ◽  
Author(s):  
S.R. Brockman ◽  
A.F. Espinosa ◽  
J.A. Michael ◽  
R. Navarro
Keyword(s):  
Near Field ◽  
Strong Motion ◽  
Strong Motion Data ◽  
Motion Data ◽  
Nuclear Explosive

Processed strong-motion data for the El Salvador earthquake of June 19, 1982

1988 ◽  
Author(s):  
Kenneth W. Campbell ◽  
Sylvester Theodore Algermissen
Keyword(s):  
El Salvador ◽  
Strong Motion ◽  
Strong Motion Data ◽  
Motion Data

Duration prediction of Chilean strong motion data using machine learning

2021 ◽  
Vol 109 ◽  
pp. 103253
Author(s):  
Sarit Chanda ◽  
M.C. Raghucharan ◽  
K.S.K. Karthik Reddy ◽  
Vasudeo Chaudhari ◽  
Surendra Nadh Somala
Keyword(s):  
Machine Learning ◽  
Strong Motion ◽  
Strong Motion Data ◽  
Motion Data ◽  
Duration Prediction
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