New Magnitude Proxy for Earthquake Early Warning Based on Initial Time Series and Frequency

2021 ◽  
Author(s):  
Yanwei Wang ◽  
Xiaojun Li ◽  
Li Li ◽  
Zifa Wang ◽  
Jingyan Lan
Keyword(s):  
Magnitude Estimation ◽  
Early Warning ◽  
Magnitude Estimate ◽  
Characteristic Parameter ◽  
Earthquake Early Warning ◽  
Initial Time ◽  
Peak Displacement ◽  
Hypocentral Distance ◽  
Comparison Results ◽  
Initial Time Series

Abstract A new characteristic parameter Sdτ is proposed to improve the performance of magnitude estimation in earthquake early warning (EEW). Sdτ is the product of summation of absolute displacement multiplied by the maximum predominant period (τmaxP) for the first arriving seconds of a seismic wave. About 30,725 underground records at borehole stations for 3645 earthquakes with magnitude between 4.0 and 9.0 from the Japanese KiK-net were used to compare the magnitude proxy performance based on the proposed Sdτ with that based on either τmaxP or peak displacement Pd. The comparison results show that for a magnitude between 4.0 and 7.3, Sdτ has a better correlation with magnitude and higher estimated accuracy than either τmaxP or Pd. Hypocentral distance is not required when using Sdτ, but it can be used to further improve the accuracy of magnitude estimate. These results confirm that Sdτ can significantly improve the accuracy and timeliness of continuous magnitude estimation in an EEW system.

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 Earthquake Early Warning System for Structural Drift Prediction Using Machine Learning and Linear Regressors

2021 ◽  
Vol 9 ◽  
Author(s):  
Antonio Giovanni Iaccarino ◽  
Philippe Gueguen ◽  
Matteo Picozzi ◽  
Subash Ghimire
Keyword(s):  
Machine Learning ◽  
Reinforced Concrete ◽  
Early Warning ◽  
Earthquake Early Warning ◽  
P Wave ◽  
Single Type ◽  
Gradient Boosting ◽  
Support Vector ◽  
Peak Displacement ◽  
Steel Reinforced Concrete

In this work, we explored the feasibility of predicting the structural drift from the first seconds of P-wave signals for On-site Earthquake Early Warning (EEW) applications. To this purpose, we investigated the performance of both linear least square regression (LSR) and four non-linear machine learning (ML) models: Random Forest, Gradient Boosting, Support Vector Machines and K-Nearest Neighbors. Furthermore, we also explore the applicability of the models calibrated for a region to another one. The LSR and ML models are calibrated and validated using a dataset of ∼6,000 waveforms recorded within 34 Japanese structures with three different type of construction (steel, reinforced concrete, and steel-reinforced concrete), and a smaller one of data recorded at US buildings (69 buildings, 240 waveforms). As EEW information, we considered three P-wave parameters (the peak displacement, Pd, the integral of squared velocity, IV2, and displacement, ID2) using three time-windows (i.e., 1, 2, and 3 s), for a total of nine features to predict the drift ratio as structural response. The Japanese dataset is used to calibrate the LSR and ML models and to study their capability to predict the structural drift. We explored different subsets of the Japanese dataset (i.e., one building, one single type of construction, the entire dataset. We found that the variability of both ground motion and buildings response can affect the drift predictions robustness. In particular, the predictions accuracy worsens with the complexity of the dataset in terms of building and event variability. Our results show that ML techniques perform always better than LSR models, likely due to the complex connections between features and the natural non-linearity of the data. Furthermore, we show that by implementing a residuals analysis, the main sources of drift variability can be identified. Finally, the models trained on the Japanese dataset are applied the US dataset. In our application, we found that the exporting EEW models worsen the prediction variability, but also that by including correction terms as function of the magnitude can strongly mitigate such problem. In other words, our results show that the drift for US buildings can be predicted by minor tweaks to models.

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.

Performance of a Hybrid Demonstration Earthquake Early Warning System in the Sichuan–Yunnan Border Region

2020 ◽  
Vol 91 (2A) ◽  
pp. 835-846
Author(s):  
Chaoyong Peng ◽  
Qiang Ma ◽  
Peng Jiang ◽  
Wenhui Huang ◽  
Dake Yang ◽  
...  
Keyword(s):  
Magnitude Estimation ◽  
Early Warning ◽  
Risk Mitigation ◽  
Effective Means ◽  
Early Warning Systems ◽  
Test Period ◽  
Earthquake Early Warning ◽  
Border Region ◽  
Hybrid Network ◽  
Origin Time

Abstract Earthquake early warning systems (EEWSs) are considered to be one of the most effective means for seismic risk mitigation, in terms of both losses and societal resilience, by releasing an alarm immediately after an earthquake occurs and before strong ground shaking arrives the target sites to be protected. To gain experience for the National System for Fast Seismic Intensity Report and Earthquake Early Warning project, we deployed a hybrid demonstration EEWS in the Sichuan–Yunnan border region with micro-electro-mechanical system-based sensors and broadband seismographs and low-latency data transmission. In this study, we described the structure of this EEWS and analyzed its performance in the first 2 yr from January 2017 to December 2018. During this test period, the EEWS detected and processed a total of 126 ML 3.0+ earthquakes, with excellent epicentral location and magnitude estimation. The average location and magnitude estimation errors for the first alert were 4.2±7.1  km and 0.2±0.31, respectively. For the earthquakes that occurred inside and outside the hybrid network, the first alert was generated 13.4±5.1  s and 26.3±13.5  s after the origin time (OT), respectively. We analyzed the performance of the EEWS for the 31 October 2018 M 5.1 earthquake, because it was the largest event that occurred inside the hybrid network during the test period. The first alert was obtained at 7.5 s after the OT, with a magnitude error of 0.1 magnitude unit, a location error of about 1 km, and a depth error of 8 km. Finally, we discussed the main differences between the EEWS’s estimates and the catalogs obtained by the China Earthquake Network Center, and proposed improvements to reduce the reporting time. This study demonstrated that we constructed a reliable, effective hybrid EEWS for the test region, which can provide sufficient support for the design of the National EEWS project.

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.

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