Practical Site-Specific Earthquake Early Warning Application

2009 ◽  
Vol 4 (4) ◽  
pp. 579-587 ◽  
Author(s):  
Katsuhisa Kanda ◽  
◽  
Tadashi Nasu ◽  
Masamitsu Miyamura
Keyword(s):  
Early Warning ◽  
Estimation Error ◽  
Seismic Intensity ◽  
Earthquake Early Warning ◽  
P Wave ◽  
Japan Meteorological Agency ◽  
Estimation Accuracy ◽  
Intensity Estimation ◽  
Site Specific ◽  
Wide Range

Real-time hazard mitigation we have developed using earthquake early warning (EEW) (1) enhances seismic intensity estimation accuracy and (2) extends the interval between when an EEW is issued and when strong tremors arrive. We accomplished the first point (enhancing seismic intensity estimation) by reducing estimation error to less than that commonly used based on an attenuation relationship and soil amplification factor by considering source-location and wave propagation path differences based on site-specific empiricism. We accomplished the second point (shortening the time between warnings and when tremors arrive) using a high-speed, reliable communication network for receiving EEW information from the Japan Meteorological Agency (JMA) and quickly transmitting warning signals to users. In areas close to quake epicenters, however, warnings may not arrive before the arrival of strong ground motions. The on-site warning system we developed uses P-wave pickup sensors that detect P-wave arrival at a site and predict seismic intensity of subsequent S-waves. We confirmed the on-site warning prototype’s feasibility based on numerical simulation and observation. We also developed an integration server for combining on-site warnings with JMA information to be applied to earthquakes over a wide range of distances. We installed a practical prototype at a construction site near the 2008 Iwate-Miyagi Inland Earthquake epicenter to measure its aftershocks because JMA EEW information was too late to use against the main shock. We obtained good aftershock results, confirming the prototype’s applicability and accuracy. Integration server combination logic was developed for manufacturing sites requiring highly robust, reliable control.

The Extended Integrated Particle Filter Method (IPFx) as a High-Performance Earthquake Early Warning System

2021 ◽  
Author(s):  
Masumi Yamada ◽  
Koji Tamaribuchi ◽  
Stephen Wu
Keyword(s):  
Particle Filter ◽  
Early Warning ◽  
Seismic Intensity ◽  
Earthquake Early Warning ◽  
Japan Meteorological Agency ◽  
Estimation Accuracy ◽  
Filter Method ◽  
Source Estimation ◽  
Single Station ◽  
Seismic Networks

ABSTRACT An earthquake early warning (EEW) system rapidly analyzes seismic data to report the occurrence of an earthquake before strong shaking is felt at a site. In Japan, the integrated particle filter (IPF) method, a new source-estimation algorithm, was recently incorporated into the EEW system to improve the source-estimation accuracy during active seismicity. The problem of the current IPF method is that it uses the trigger information computed at each station in a specific format as the input and is therefore applicable to only limited seismic networks. This study proposes the extended IPF (IPFx) method to deal with continuous waveforms and merge all Japanese real-time seismic networks into a single framework. The new source determination algorithm processes seismic waveforms in two stages. The first stage (single-station processing) extracts trigger and amplitude information from continuous waveforms. The second stage (network processing) accumulates information from multiple stations and estimates the location and magnitude of ongoing earthquakes based on Bayesian inference. In 10 months of continuous online experiments, the IPFx method showed good performance in detecting earthquakes with maximum seismic intensity ≥3 in the Japan Meteorological Agency (JMA) catalog. By merging multiple seismic networks into a single EEW system, the warning time of the current EEW system can be improved further. The IPFx method provides accurate shaking estimation even at the beginning of event detection and achieves seismic intensity error <0.25  s after detecting an event. This method correctly avoided two major false alarms on 5 January 2018 and 30 July 2020. The IPFx method offers the potential of expanding the JMA IPF method to global seismic networks.

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.

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.

scholarly journals Applicability of Accurate Ground Motion Estimation Using Initial P Wave for Earthquake Early Warning

2021 ◽  
Vol 9 ◽  
Author(s):  
Zijun Wang ◽  
Boming Zhao
Keyword(s):  
Motion Estimation ◽  
Ground Motion ◽  
Early Warning ◽  
Earthquake Early Warning ◽  
P Wave ◽  
Peak Ground Velocity ◽  
Estimation Accuracy ◽  
Economic Losses ◽  
Robust Estimator ◽  
Characteristic Parameters

The earthquake early warning (EEW) system is capable of mitigating seismic hazards and reducing deaths, injuries, and economic losses. Although EEW approaches have already been developed worldwide, improving the accuracy and applicability is still controversial. Aiming at the ground motion estimation using the initial P wave, we investigated eight representative characteristic parameters, i.e., the peak measurements and integral quantities, using the database of the 2008 Wenchuan earthquake, where the aftershocks with the criteria that 4.0 ≤ Ms ≤ 6.5 and epicentral distance less than 150 km are analyzed. We established the relationships between the eight characteristic parameters and four ground motion parameters, respectively, based on which the estimation accuracy and reliability and the extent to which the increasingly expanding time windows could affect the estimates are analyzed accordingly. We found that the integral quantities could also be a robust estimator for peak ground acceleration (PGA), peak ground velocity (PGV), and spectral intensity (SI), while the peak measurement is more useful in estimating peak ground displacement (PGD). In addition, for estimating the ground motion of events with magnitudes less than 6.5, a 2-s window could effectively improve the estimation accuracy by approximately 11.5–18.5% compared with using a 1-s window, as the window increases to 3 s, the accuracy would further improve while the growth rate will be reduced to around 3.0–8.0%.

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.

End-to-end PGA estimation for earthquake early warning using transformer networks

2020 ◽  
Author(s):  
Jannes Münchmeyer ◽  
Dino Bindi ◽  
Ulf Leser ◽  
Frederik Tilmann
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Ground Motion ◽  
Early Warning ◽  
Earthquake Early Warning ◽  
P Wave ◽  
Lead Times ◽  
Ground Shaking ◽  
Target Sites ◽  
End To End

<p>The key task of earthquake early warning is to provide timely and accurate estimates of the ground shaking at target sites. Current approaches use either source or propagation based methods. Source based methods calculate fast estimates of the earthquake source parameters and apply ground motion prediction equations to estimate shaking. They suffer from saturation effects for large events, simplified assumptions and the need for a well known hypocentral location, which usually requires arrivals at multiple stations. Propagation based methods estimate levels of shaking from the shaking at neighboring stations and therefore have short warning times and possibly large blind zones. Both methods only use specific features from the waveform. In contrast, we present a multi-station neural network method to estimate horizontal peak ground acceleration (PGA) anywhere in the target region directly from raw accelerometer waveforms in real time.</p><p>The three main components of our model are a convolutional neural network (CNN) for extracting features from the single-station three-component accelerograms, a transformer network for combining features from multiple stations and for transferring them to the target site features and a mixture density network to generate probabilistic PGA estimates. By using a transformer network, our model is able to handle a varying set and number of stations as well as target sites. We train our model end-to-end using recorded waveforms and PGAs. We use data augmentation to enable the model to provide estimations at targets without waveform recordings. Starting with the arrival of a P wave at any station of the network, our model issues real-time predictions at each new sample. The predictions are Gaussian mixtures, giving estimates of both expected value and uncertainties. The model can be used to predict PGA at specific target sites, as well as to generate ground motion maps.</p><p>We analyze the model on two strong motion data sets from Japan and Italy in terms of standard deviation and lead times. Through the probabilistic predictions we are able to give lead times for different levels of uncertainty and ground shaking. This allows to control the ratio of missed detections to false alerts. Preliminary analysis suggest that for levels between 1%g and 10%g our model achieves multi-second lead times even for the closest stations at a false-positive rate below 25%. For an example event at 50 km depth, lead times at the closest stations with epicentral distances below 20 km are 6 s and 7.5 s. This suggests that our model is able to effectively use the difference between P and S travel time and accurately assess the future level of ground shaking from the first parts of the P wave. It additionally makes effective use of the information contained in the absence of signal at other stations.</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.

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