DEVELOPMENT OF EARTHQUAKE DISASTER PREVENTION SYSTEM USING BOTH EARTHQUAKE EARLY WARNING AND INFORMATION OF P-WAVE FROM ONSITE SEISMOMETER AND ITS PERFORMANCE

We analyzed strong-motion records at the ground and borehole in and around the Kanto Basin and the seafloor in the Japan Trench area from three nearby offshore earthquakes of similar magnitudes (Mw 5.8–5.9). The seafloor strong-motion records were obtained from S-net, which was established to enhance tsunami and earthquake early warnings after the 2011 great Tohoku-oki earthquake disaster. The borehole records were obtained from MeSO-net, a dense network of seismometers installed at a depth of 20 m in the Tokyo metropolitan area. The ground records were obtained from the K-NET and KiK-net networks, established after the 1995 great Hanshin-Awaji earthquake disaster. The MeSO-net and S-net stations record the shakings continuously, while the K-NET and KiK-net records are based on triggering thresholds. It is crucial to evaluate the properties of strong motions recorded by the S-net for earthquake early warning (EEW). This paper compared the peak ground accelerations (PGAs) and peak ground velocities (PGVs) between the S-net and K-NET/KiK-net stations. Because the MeSO-net records were from the borehole, we compared the PGAs and significant durations of the low-frequency motions (0.1–0.5 Hz) between the S-net and MeSO-net stations from identical record lengths. We found that the horizontal PGAs and PGVs at the S-net sites were similar to or larger than the K-NET/KiK-net sites for the S wave. In contrast, the vertical PGAs and PGVs at the S-net sites were similar to or smaller than those at the K-NET/KiK-net sites for the S wave. Particularly, the PGAs and PGVs for the P-wave parts on the vertical-component records of S-net were, on average, much smaller than those of K-NET/KiK-net records. The difference was more evident in the PGAs. The average ratios of S-wave horizontal to vertical PGAs were about 2.5 and 5 for the land and S-net sites, respectively. The low-frequency PGAs at the S-net sites were similar to or larger than those of the MeSO-net borehole records. The significant durations between the two-networks low-frequency records were generally comparable. Quantification of the results from a larger dataset may contribute to ground-motion prediction for EEW and the design of the offshore facilities.

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

10.5194/egusphere-egu2020-5107 ◽

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

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.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.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.

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Practical Site-Specific Earthquake Early Warning Application

Journal of Disaster Research ◽

10.20965/jdr.2009.p0251 ◽

2009 ◽

Vol 4 (4) ◽

pp. 579-587 ◽

Cited By ~ 4

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.

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Magnitude estimation using initial P-wave amplitude and its spatial distribution in earthquake early warning in Taiwan

Geophysical Research Letters ◽

10.1029/2011gl047461 ◽

2011 ◽

Vol 38 (9) ◽

pp. n/a-n/a ◽

Cited By ~ 4

Author(s):

Ting-Li Lin ◽

Yih-Min Wu ◽

Da-Yi Chen

Keyword(s):

Spatial Distribution ◽

Magnitude Estimation ◽

Early Warning ◽

Wave Amplitude ◽

Earthquake Early Warning ◽

P Wave

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

Frontiers in Earth Science ◽

10.3389/feart.2021.666444 ◽

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

Geophysical Journal International ◽

10.1093/gji/ggab349 ◽

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.

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

10.5194/egusphere-egu21-13876 ◽

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

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.

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

Seismological Research Letters ◽

10.1785/0220210144 ◽

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.

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Earthquake Early Warning System Application for School Disaster Prevention

Journal of Disaster Research ◽

10.20965/jdr.2009.x0557 ◽

2009 ◽

Vol 4 (4) ◽

pp. 557-564 ◽

Cited By ~ 3

Author(s):

Masato Motosaka ◽

◽

Makoto Homma

Keyword(s):

Junior High School ◽

Early Warning ◽

Warning System ◽

Early Warning Systems ◽

Lessons Learned ◽

Earthquake Early Warning ◽

Disaster Prevention ◽

Earthquake Early Warning System ◽

Miyagi Prefecture ◽

School Disaster

Earthquake early warning systems (EEWS) based on nationwide earthquake observation networks in Japan are applied to earthquake damage mitigation in many fields. The authors have developed a real-time earthquake information (RTEI) application for school disaster prevention. This paper describes, first an EEWS demonstration test at an elementary school in Sendai and extended demonstration tests for three other elementary schools and a junior high school. Then, the transmission of RTEI using an intranet connecting schools is described, together with demonstration test using the Schools in Wide Area Networks in Miyagi Prefecture (Miyagi-SWAN), Japan. These demonstration tests were conducted as part of a government project. This paper also addresses a questionnaire investigation on EEWS applications in schools which was also conducted in the project. The experience of a real earthquake, the June 14, 2008, Iwate-Miyagi Nairiku Earthquake (M7.2), is then described. Lessons learned from the demonstration tests in schools are summarized.

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.256-259.2775 ◽

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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