Seismotectonic Crustal Strains of the Mongol-Baikal Seismic Belt from Seismological Data

2018 ◽  
pp. 497-517 ◽  
Author(s):  
Alena Seredkina ◽  
Valentina Melnikova
Keyword(s):  
Seismic Belt ◽  
Seismological Data

Puerto Rico seismic program seismological data summary, July 1, 1975-December 31, 1977

1979 ◽  
Author(s):  
R.L. Dart ◽  
David Carver ◽  
M.K. Wharton ◽  
A.C. Tarr
Keyword(s):  
Puerto Rico ◽  
Seismological Data

Transit of Catastrophic Water-Stone Debris Flow on the River Kyngarga (Republic Buryatia) on June 27-29, 2014 According to Seismological Data

2018 ◽  
Vol 481 (6) ◽  
pp. 675-679
Author(s):  
V. Chechelnitsky ◽  
S. Makarov ◽  
A. Dobrynina ◽  
◽  
◽  
...  
Keyword(s):  
Debris Flow ◽  
Seismological Data

Using modern seismological data to reveal earthquake precursors

2007 ◽  
Vol 9 (2) ◽  
pp. 1-17 ◽  
Author(s):  
G. A. Sobolev ◽  
A. A. Lyubushin
Keyword(s):  
Earthquake Precursors ◽  
Seismological Data

Postseismic Mantle Relaxation in the Central Nevada Seismic Belt

Science ◽  
2005 ◽  
Vol 310 (5753) ◽  
pp. 1473-1476 ◽  
Author(s):  
N. Gourmelen
Keyword(s):  
Seismic Belt

scholarly journals Analyzing the Performance of GPS Data for Earthquake Prediction

2021 ◽  
Vol 13 (9) ◽  
pp. 1842
Author(s):  
Valeri Gitis ◽  
Alexander Derendyaev ◽  
Konstantin Petrov
Keyword(s):  
Earthquake Prediction ◽  
Spatial Density ◽  
Gps Data ◽  
Quality Of Data ◽  
Minimum Area ◽  
Seismological Data ◽  
Successful Prediction ◽  
Spatio Temporal ◽  
Hypocenter Depth

The results of earthquake prediction largely depend on the quality of data and the methods of their joint processing. At present, for a number of regions, it is possible, in addition to data from earthquake catalogs, to use space geodesy data obtained with the help of GPS. The purpose of our study is to evaluate the efficiency of using the time series of displacements of the Earth’s surface according to GPS data for the systematic prediction of earthquakes. The criterion of efficiency is the probability of successful prediction of an earthquake with a limited size of the alarm zone. We use a machine learning method, namely the method of the minimum area of alarm, to predict earthquakes with a magnitude greater than 6.0 and a hypocenter depth of up to 60 km, which occurred from 2016 to 2020 in Japan, and earthquakes with a magnitude greater than 5.5. and a hypocenter depth of up to 60 km, which happened from 2013 to 2020 in California. For each region, we compare the following results: random forecast of earthquakes, forecast obtained with the field of spatial density of earthquake epicenters, forecast obtained with spatio-temporal fields based on GPS data, based on seismological data, and based on combined GPS data and seismological data. The results confirm the effectiveness of using GPS data for the systematic prediction of earthquakes.

scholarly journals Active Fault Systems in the Inner Northwest Apennines, Italy: A Reappraisal One Century after the 1920 Mw ~6.5 Fivizzano Earthquake

Geosciences ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 139
Author(s):  
Giancarlo Molli ◽  
Isabelle Manighetti ◽  
Rick Bennett ◽  
Jacques Malavieille ◽  
Enrico Serpelloni ◽  
...  
Keyword(s):  
Large Scale ◽  
Active Fault ◽  
Active Faults ◽  
Dense Network ◽  
Earthquake Fault ◽  
Slip Rates ◽  
Fault Systems ◽  
Seismological Data ◽  
Seismogenic Faults

Based on the review of the available stratigraphic, tectonic, morphological, geodetic, and seismological data, along with new structural observations, we present a reappraisal of the potential seismogenic faults and fault systems in the inner northwest Apennines, Italy, which was the site, one century ago, of the devastating Mw ~6.5, 1920 Fivizzano earthquake. Our updated fault catalog provides the fault locations, as well as the description of their architecture, large-scale segmentation, cumulative displacements, evidence for recent to present activity, and long-term slip rates. Our work documents that a dense network of active faults, and thus potential earthquake fault sources, exists in the region. We discuss the seismogenic potential of these faults, and propose a general tectonic scenario that might account for their development.

Wavelet analysis of the temporal-spatial distribution in the Eurasia seismic belt

2017 ◽  
Vol 15 (03) ◽  
pp. 1750018 ◽  
Author(s):  
Wenfeng Zheng ◽  
Xiaolu Li ◽  
Lirong Yin ◽  
Zhengtong Yin ◽  
Bo Yang ◽  
...  
Keyword(s):  
Fault Zone ◽  
Compact Support ◽  
Large Scale ◽  
Temporal Distribution ◽  
Seismic Energy ◽  
Research Area ◽  
Earthquake Activity ◽  
Seismic Zone ◽  
Seismic Belt ◽  
Earthquake Fault

Due to the growing frequency of earthquakes, safeties of human lives and properties are facing serious threats. However, the research in the field of spatial-temporal distribution of earthquake is quite a few. In this paper, we use wavelet model to analyze the spatial-temporal distribution of earthquakes. Because the spatial-temporal distribution of earthquake activity is closely related to the distribution of the earthquake fault zone, we analyze large-scale earthquake clusters by selecting the Eurasia seismic belt and the surrounding region as the research area. From the perspective of the time domain, the results show that the seismic energy of the earthquake fault zone presences compact support or similar compact support distribution, suggesting that the seismic zone exists a relatively quiet period and active stage. This indicate that the seismic zone is periodical. The period of strong earthquakes above normal and less than normal is different by time changes. The cycles of earthquakes are different due to different regions and different geological and geographical environment.

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