Earthquake Prediction Based on Combined Seismic and GPS Monitoring Data

2021 ◽  
pp. 601-612
Author(s):  
V. G. Gitis ◽  
A. B. Derendyaev ◽  
K. N. Petrov
Keyword(s):  
Earthquake Prediction ◽  
Monitoring Data ◽  
Gps Monitoring

GPS monitoring and earthquake prediction: A success story towards a useful integration

2009 ◽  
Vol 465 (1-4) ◽  
pp. 177-189 ◽  
Author(s):  
Alessandra Borghi ◽  
Abdelkrim Aoudia ◽  
Riccardo E.M. Riva ◽  
Riccardo Barzaghi
Keyword(s):  
Earthquake Prediction ◽  
Success Story ◽  
Gps Monitoring

Application of GPS monitoring data in landslide prediction

2008 ◽  
Author(s):  
Deying Li ◽  
Kunlong Yin ◽  
Lixia Chen ◽  
Wenming Chen
Keyword(s):  
Monitoring Data ◽  
Landslide Prediction ◽  
Gps Monitoring

Study on the SVM Processing Model of the GPS Monitoring Data of Coal Mine Subsidence

2014 ◽  
Vol 598 ◽  
pp. 436-441
Author(s):  
Jun Zhang
Keyword(s):  
Coal Mine ◽  
Polynomial Function ◽  
Monitoring Data ◽  
Support Vector ◽  
Engineering Practice ◽  
Mine Subsidence ◽  
Calculating Method ◽  
Function Fitting ◽  
Gps Monitoring ◽  
The One

In order to make the GPS monitoring data of coal mine subsidence useful and effective in engineering practice, this paper tries to analyze the exceptional handling processing model of the GPS monitoring data of coal mine subsidence under the guidance of the principle of support vector machine (SVM) regression, its calculating method and the application of regression program produced by MATLAB. By comparing the result of the exceptional handling processing model established on practical measured data with the one of the polynomial function fitting, this thesis proves that the application of vector regression algorithm in studies on the exceptional handling processing model of the GPS monitoring data is highly effective.

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