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

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

Frontiers in Earth Science ◽

10.3389/feart.2021.653226 ◽

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.

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

Scientific Reports ◽

10.1038/s41598-020-78046-2 ◽

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.256-259.2193 ◽

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.

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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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A comparison ofτcandτpmaxfor magnitude estimation in earthquake early warning

Geophysical Research Letters ◽

10.1029/2008gl035611 ◽

2008 ◽

Vol 35 (20) ◽

Cited By ~ 24

Author(s):

Jang-Tian Shieh ◽

Yih-Min Wu ◽

Richard M. Allen

Keyword(s):

Magnitude Estimation ◽

Early Warning ◽

Earthquake Early Warning

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