A Comparison of Two Magnitude Estimation Methods Using Predominant Period in Earthquake Early Warning

2012 ◽  
Vol 256-259 ◽  
pp. 2193-2199
Author(s):  
Jin Dong Song ◽  
Shan You Li
Keyword(s):  
Magnitude Estimation ◽  
Early Warning ◽  
Earthquake Early Warning ◽  
P Wave ◽  
Estimation Methods ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Predominant Period ◽  
Low Pass ◽  
Wave Arrival

Currently, there are two magnitude estimation approaches using predominant period for earthquake early warning, Tpmax method and Tc method. We compared Tpmax method with Tc method from the NSMP strong motion records of 22 earthquakes in United States with moment magnitude ranging from 4.1 to 7.9, to explore which method was with higher precision and could be suitable for the earthquake early warning system. Our results show that scaling relations between the two predominant period parameters, Tpmax and Tc, calculated from P-wave arrivals and earthquake magnitude are consistent with previous research. It was found that Tc method had higher precision than Tpmax method with the same filter band, and had best result in magnitude estimation resulting in a correlation coefficient of 0.78 and a standard deviation of 0.16 using 3 seconds signal after the P-wave arrival. We also found that Tc method without low-pass filter had higher accuracy than Tpmax method without low-pass filter. We recommended Tc method using 3 seconds signal after the P-wave arrival as the priority magnitude estimation method for earthquake early warning.

Magnitude Estimation for Earthquake Early Warning with Multiple Parameter Inputs and a Support Vector Machine

2021 ◽  
Author(s):  
Jingbao Zhu ◽  
Shanyou Li ◽  
Jindong Song
Keyword(s):  
Support Vector Machine ◽  
Magnitude Estimation ◽  
Early Warning ◽  
Epicentral Distance ◽  
Estimation Error ◽  
Earthquake Early Warning ◽  
P Wave ◽  
Support Vector ◽  
Multiple Parameter ◽  
Wave Arrival

Abstract Accurately estimating the magnitude within the initial seconds after the P-wave arrival is of great significance in earthquake early warning (EEW). Over the past few decades, single-parameter approaches such as the τc and Pd methods have been applied to EEW magnitude estimation studies considering the first 3 s after the P-wave onset. However, these methods present considerable scatter and are affected by the signal-to-noise ratio (SNR) and epicentral distance. In this study, using Japanese K-NET strong-motion data, we propose a machine-learning method comprising multiple parameter inputs, namely, the support vector machine magnitude estimation (SVM-M) model, to determine earthquake magnitudes and resolve the aforementioned problems. Our results using a single seismological station record show that the standard deviation of the magnitude prediction errors of the SVM-M model is 0.297, which is less than those of the τc (1.637) and Pd (0.425) methods. The magnitudes estimated by the SVM-M model within 3 s after the P-wave arrival are not obviously affected by the SNR or epicentral distance, and not overestimated for MJMA≤5. In addition, in an offline EEW application, the magnitude estimation error of the SVM-M model gradually decreases with increasing time after the first station is triggered, and the underestimation of event magnitudes for 6.5≤MJMA gradually improves. These results demonstrate that the proposed SVM-M model can robustly estimate earthquake magnitudes and has potential for EEW.

An Improved Magnitude Estimation Method Using Combination of Predominant Period and Peak Amplitude for Earthquake Early Warning

2012 ◽  
Vol 256-259 ◽  
pp. 2775-2780
Author(s):  
Jin Dong Song ◽  
Shan You Li
Keyword(s):  
Magnitude Estimation ◽  
Early Warning ◽  
Estimation Method ◽  
Strong Motion ◽  
Earthquake Early Warning ◽  
P Wave ◽  
Peak Amplitude ◽  
Combined Method ◽  
Predominant Period ◽  
Great Magnitude

The critical technology of Earthquake Early Warning (EEW) is determining the size of an earthquake and the predicted ground motion at given site, from the first few seconds of the P wave arrivals. Currently, there were two different approaches to the EEW magnitude estimation, the predominant period method and the peak amplitude method. However, both methods mentioned above had some disadvantages, such as significant uncertainty and saturation at great magnitude. To improve the results of magnitude estimation, a combined method using predominant period τc and peak amplitude of acceleration Pmax was introduced. Compared with the predominant period method and the peak amplitude method, the estimation standard deviation level of the combined method is 0.42 using NSMP strong motion data. The magnitude estimation results of the first three seconds P wave indicate that, the estimation precision of combined method is higher than those of the two methods, the predominant period method and the peak amplitude method, and the saturation at great magnitude is improved.

scholarly journals Magnitude Estimation for Earthquake Early Warning Using a Deep Convolutional Neural Network

2021 ◽  
Vol 9 ◽  
Author(s):  
Jingbao Zhu ◽  
Shanyou Li ◽  
Jindong Song ◽  
Yuan Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Magnitude Estimation ◽  
Early Warning ◽  
Epicentral Distance ◽  
Estimation Error ◽  
Deep Convolutional Neural Network ◽  
Earthquake Early Warning ◽  
P Wave ◽  
Wave Arrival

Magnitude estimation is a vital task within earthquake early warning (EEW) systems (EEWSs). To improve the magnitude determination accuracy after P-wave arrival, we introduce an advanced magnitude prediction model that uses a deep convolutional neural network for earthquake magnitude estimation (DCNN-M). In this paper, we use the inland strong-motion data obtained from the Japan Kyoshin Network (K-NET) to calculate the input parameters of the DCNN-M model. The DCNN-M model uses 12 parameters extracted from 3 s of seismic data recorded after P-wave arrival as the input, four convolutional layers, four pooling layers, four batch normalization layers, three fully connected layers, the Adam optimizer, and an output. Our results show that the standard deviation of the magnitude estimation error of the DCNN-M model is 0.31, which is significantly less than the values of 1.56 and 0.42 for the τc method and Pd method, respectively. In addition, the magnitude prediction error of the DCNN-M model is not affected by variations in the epicentral distance. The DCNN-M model has considerable potential application in EEWSs in Japan.

scholarly journals P-wave picking for earthquake early warning: Refinement of a T pd method

2021 ◽  
Author(s):  
Masumi Yamada ◽  
Jim Mori
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Arrival Time ◽  
Early Warning Systems ◽  
Earthquake Early Warning ◽  
P Wave ◽  
Warning Systems ◽  
Wave Detection ◽  
Earthquake Early Warning Systems ◽  
Wave Arrival

Summary Detecting P-wave onsets for on-line processing is an important component for real-time seismology. As earthquake early warning systems around the world come into operation, the importance of reliable P-wave detection has increased, since the accuracy of the earthquake information depends primarily on the quality of the detection. In addition to the accuracy of arrival time determination, the robustness in the presence of noise and the speed of detection are important factors in the methods used for the earthquake early warning. In this paper, we tried to improve the P-wave detection method designed for real-time processing of continuous waveforms. We used the new Tpd method, and proposed a refinement algorithm to determine the P-wave arrival time. Applying the refinement process substantially decreases the errors of the P-wave arrival time. Using 606 strong motion records of the 2011 Tohoku earthquake sequence to test the refinement methods, the median of the error was decreased from 0.15 s to 0.04 s. Only three P-wave arrivals were missed by the best threshold. Our results show that the Tpd method provides better accuracy for estimating the P-wave arrival time compared to the STA/LTA method. The Tpd method also shows better performance in detecting the P-wave arrivals of the target earthquakes in the presence of noise and coda of previous earthquakes. The Tpd method can be computed quickly so it would be suitable for the implementation in earthquake early warning systems.

Station Correction of P-Alert Network to Improve Magnitude Estimation for Earthquake Early Warning

2021 ◽  
Author(s):  
Yu-Ting Wu ◽  
Yih-Min Wu
Keyword(s):  
Regression Model ◽  
Magnitude Estimation ◽  
Early Warning ◽  
Latent Variables ◽  
Regression Method ◽  
Earthquake Early Warning ◽  
P Wave ◽  
Station Correction ◽  
Hypocentral Distance ◽  
Station Corrections

<p>Magnitude estimation for earthquake early warning has been shown that it can be achieved by utilizing the relationship among the first three seconds P-wave amplitude, hypocentral distance and magnitude. However, the regression models in previous studies about P-Alert didn't include station correction factors, which may cause non-negligible effects. Thus, to improve the precision of magnitude estimation, we take station corrections into consideration when building the regression model. For the reason that station corrections are the unobserved latent variables of the model, we adopt the iteration regression method, which is based on the expectation-maximization algorithm, to determine them. By using this method, we are able to approach the values of both the station corrections and the coefficients of the regression model after several iterations. Our preliminary results show that after utilizing the iteration regression method, the standard deviation reduces from 0.30 to 0.26, and the station corrections we get range from -0.70 to 0.66.</p>

scholarly journals The Substitution Elasticity, Factor Shares, and the Low-Frequency Panel Model

2017 ◽  
Vol 9 (4) ◽  
pp. 225-253 ◽  
Author(s):  
Robert S. Chirinko ◽  
Debdulal Mallick
Keyword(s):  
Low Frequency ◽  
Estimation Methods ◽  
Function Estimation ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Long Run ◽  
New Strategy ◽  
Low Pass ◽  
Secular Decline ◽  
Production Function Estimation

The value of the elasticity of substitution between labor and capital (σ) is a crucial assumption in understanding the secular decline in the labor share of income. This paper develops and implements a new strategy for estimating this crucial parameter by combining a low-pass filter with panel data to identify the low-frequency/long-run relations appropriate to production function estimation. Standard estimation methods, which do not filter out transitory variation, generate downwardly biased estimates of 40 percent to 70 percent relative to the benchmark value. Despite correcting for this bias, our preferred estimate of 0.40 is substantially below the Cobb-Douglas assumption of σ = 1. (JEL C51, E22, E24, E25, O41)

Deep learning for P-wave arrival picking in earthquake early warning

2021 ◽  
Vol 20 (2) ◽  
pp. 391-402
Author(s):  
Wang Yanwei ◽  
Li Xiaojun ◽  
Wang Zifa ◽  
Shi Jianping ◽  
Bao Enhe
Keyword(s):  
Deep Learning ◽  
Early Warning ◽  
Earthquake Early Warning ◽  
P Wave ◽  
Wave Arrival

scholarly journals Threshold-based evolutionary magnitude estimation for an earthquake early warning system in the Sichuan–Yunnan region, China

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yuan Wang ◽  
Shanyou Li ◽  
Jindong Song
Keyword(s):  
Magnitude Estimation ◽  
Early Warning ◽  
Time Window ◽  
Estimation Method ◽  
Earthquake Early Warning ◽  
P Wave ◽  
Estimation Accuracy ◽  
Earthquake Hazards ◽  
Yunnan Region ◽  
Low Magnitude

AbstractThe Sichuan–Yunnan region is one of the most seismically vulnerable areas in China. Accordingly, an earthquake early warning (EEW) system for the region is essential to reduce future earthquake hazards. This research analyses the utility of two early warning parameters (τc and Pd) for magnitude estimation using 273 events that occurred in the Sichuan–Yunnan region during 2007–2015. We find that τc can more reliably predict high-magnitude events during a short P-wave time window (PTW) but produces greater uncertainty in the low-magnitude range, whereas Pd is highly correlated with the event magnitude depending on the selection of an appropriate PTW. Here, we propose a threshold-based evolutionary magnitude estimation method based on a specific combination of τc and Pd that both offers more robust advance magnitude estimates for large earthquakes and ensures the estimation accuracy for low-magnitude events. The advantages of the proposed approach are validated using data from 2016–2017 and the Ms 8.0 Wenchuan earthquake in an offline simulation. The proposed concept provides a useful basis for the future implementation of an EEW system in the Sichuan–Yunnan region.

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