Monitoring a Building Using Deconvolution Interferometry. I: Earthquake-Data Analysis

2013 ◽  
Vol 103 (3) ◽  
pp. 1662-1678 ◽  
Author(s):  
N. Nakata ◽  
R. Snieder ◽  
S. Kuroda ◽  
S. Ito ◽  
T. Aizawa ◽  
...  
Keyword(s):  
Data Analysis ◽  
Earthquake Data ◽  
Deconvolution Interferometry

Feasibility of Vs Liquefication Evaluation Methods by Bachu Xinjiang Earthquake Data

2012 ◽  
Vol 238 ◽  
pp. 848-851
Author(s):  
Qian Yu Zhao ◽  
Rui Sun ◽  
Yu Run Li ◽  
Wei Ming Wang
Keyword(s):  
Data Analysis ◽  
Success Rate ◽  
Linear Model ◽  
Seismic Design ◽  
Real Data ◽  
Soil Liquefaction ◽  
The Other ◽  
Velocity Data ◽  
Discrimination Model ◽  
Earthquake Data

A discrimination model for soil liquefaction is established by analyzing the liquefied and non-liquefied sites in the Bachu Xinjiang earthquake, based on 44 shear wave velocity data. One of them is based on the Code for seismic design of buildings, which is a linear model. The model is brief and convenient, while the evaluation success rate is 80%. But compared with the nonlinear model, the linear model is not advanced enough. The other model is based on probability analysis, and the evaluation success rate can reach up to 93%. And the discrimination results are high in reliability rely on real data analysis.

scholarly journals A 3D Display System of Seismic-center Distribution for Earthquake Data Analysis

2005 ◽  
Vol 4 (2) ◽  
pp. 54-67 ◽  
Author(s):  
Mami Kaito ◽  
Emi Watanabe ◽  
Takatoshi Naka ◽  
Masashi Yamada ◽  
Mamoru Endo ◽  
...  
Keyword(s):  
Data Analysis ◽  
Display System ◽  
3D Display ◽  
Earthquake Data

Site Effects and Microzonation in Acapulco

1998 ◽  
Vol 14 (1) ◽  
pp. 75-93 ◽  
Author(s):  
Francisco J. Chávez-García ◽  
Julio Cuenca
Keyword(s):  
Data Analysis ◽  
Seismic Risk ◽  
Site Effects ◽  
Pacific Coast ◽  
Strong Motion ◽  
Strong Motion Records ◽  
The Pacific ◽  
Dominant Period ◽  
Seismograph Network ◽  
Earthquake Data

The region around Acapulco, on the Pacific coast of Mexico, is subjected to large seismic risk. This paper presents a contribution to improve microzonation of this region. We investigated site effects using three basic sources of data: strong-motion records from all of the instruments that have operated within the area; weak-motion records obtained from the installation and operation of a temporal, digital, seismograph network; and measurements of microtremors at 35 sites. We compared and evaluated different techniques of data analysis. We show that very coherent results are obtained from different kinds of measurement, and that microtremor records are very useful to interpolate sparse earthquake data. We propose two maps that reflect the fundamental characteristics of site effects in the area: dominant period and maximum relative amplification. These maps may be used to improve current microzonation of Acapulco.

Morphotectonic and earthquake data analysis of interactional faults in Sabzevaran Area, SE Iran

2020 ◽  
Vol 139 ◽  
pp. 104147
Author(s):  
Ahmad Rashidi ◽  
Mohammad-Reza Abbassi ◽  
Faramarz Nilfouroushan ◽  
Shahram Shafiei ◽  
Reza Derakhshani ◽  
...  
Keyword(s):  
Data Analysis ◽  
Earthquake Data

scholarly journals Laboratory earthquake forecasting: A machine learning competition

2021 ◽  
Vol 118 (5) ◽  
pp. e2011362118
Author(s):  
Paul A. Johnson ◽  
Bertrand Rouet-Leduc ◽  
Laura J. Pyrak-Nolte ◽  
Gregory C. Beroza ◽  
Chris J. Marone ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Earthquake Prediction ◽  
Seismic Data ◽  
Earthquake Forecasting ◽  
Seismic Cycle ◽  
Pedagogical Tool ◽  
Failure Times ◽  
Testing Data ◽  
Earthquake Data

Earthquake prediction, the long-sought holy grail of earthquake science, continues to confound Earth scientists. Could we make advances by crowdsourcing, drawing from the vast knowledge and creativity of the machine learning (ML) community? We used Google’s ML competition platform, Kaggle, to engage the worldwide ML community with a competition to develop and improve data analysis approaches on a forecasting problem that uses laboratory earthquake data. The competitors were tasked with predicting the time remaining before the next earthquake of successive laboratory quake events, based on only a small portion of the laboratory seismic data. The more than 4,500 participating teams created and shared more than 400 computer programs in openly accessible notebooks. Complementing the now well-known features of seismic data that map to fault criticality in the laboratory, the winning teams employed unexpected strategies based on rescaling failure times as a fraction of the seismic cycle and comparing input distribution of training and testing data. In addition to yielding scientific insights into fault processes in the laboratory and their relation with the evolution of the statistical properties of the associated seismic data, the competition serves as a pedagogical tool for teaching ML in geophysics. The approach may provide a model for other competitions in geosciences or other domains of study to help engage the ML community on problems of significance.

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