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Fast magnitude determination using a single seismological station record implementing machine learning techniques 被引量:3
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作者 Luis H.Ochoa Luis F.Nino Carlos A.Vargas 《Geodesy and Geodynamics》 2018年第1期34-41,共8页
In this work a Support Vector Machine Regression(SVMR) algorithm is used to calculate local magnitude(MI) using only five seconds of signal after the P wave onset of one three component seismic station. This algor... In this work a Support Vector Machine Regression(SVMR) algorithm is used to calculate local magnitude(MI) using only five seconds of signal after the P wave onset of one three component seismic station. This algorithm was trained with 863 records of historical earthquakes, where the input regression parameters were an exponential function of the waveform envelope estimated by least squares and the maximum value of the observed waveform for each component in a single station. Ten-fold cross validation was applied for a normalized polynomial kernel obtaining the mean absolute error for different exponents and complexity parameters. The local magnitude(MI) could be estimated with 0.19 units of mean absolute error. The proposed algorithm is easy to implement in hardware and may be used directly after the field seismological sensor to generate fast decisions at seismological control centers, increasing the possibility of having an effective reaction. 展开更多
关键词 Earthquake early warning support vector machine Regression Earthquake Rapid response local magnitude Seismic event Seismology Bogota Colombia
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