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

Fast and accurate P-wave arrival picking significantly affects the performance of earthquake early warning(EEW)systems.Automated P-wave picking algorithms used in EEW have encountered problems of falsely picking up noise,missing P-waves and inaccurate P-wave arrival estimation.To address these issues,an automatic algorithm based on the convolution neural network(DPick)was developed,and trained with a moderate number of data sets of 17,717 accelerograms.Compared to the widely used approach of the short-term average/long-term average of signal characteristic function(STA/LTA),DPick is 1.6 times less likely to detect noise as a P-wave,and 76 times less likely to miss P-waves.In terms of estimating P-wave arrival time,when the detection task is completed within 1 s,DPick′s detection occurrence is 7.4 times that of STA/LTA in the 0.05 s error band,and 1.6 times when the error band is 0.10 s.This verified that the proposed method has the potential for wide applications in EEW.

An Introductory Overview of Earthquake Early Warning

Earthquake early warning(EEW)is discriminated from earthquake prediction by using initial seismic waves to predict the severity of ground motion and issue the warning information to potential affected area.The warning information is useful to mitigate the disaster and decrease the losses of life and economy.We reviewed the development history of EEW worldwide and summarized the methodologies using in different systems.Some new sensors came and are coming into EEW giving more developing potential to future implementation.The success of earthquake disaster mitigation relies on the cooperation of the whole society.

Continuous estimates on the earthquake early warning magnitude by use of the near-field acceleration records

In this article, the seismic records of Japan＇s Kik-net are selected to measure the acceleration, displacement, and effective peak acceleration of each seismic record within a certain time after P wave, then a continuous estimation is given on earthquake early warning magnitude through statistical analysis method, and Wenchuan earthquake record is utilized to check the method. The results show that the reliability of earthquake early warning magnitude continuously increases with the increase of the seismic information, the biggest residual happens if the acceleration is adopted to fit earthquake magnitude, which may be caused by rich high-frequency components and large dispersion of peak value in acceleration record, the influence caused by the high-frequency components can be effectively reduced if the effective peak acceleration and peak displacement is adopted, it is estimated that the dispersion of earthquake magnitude obviously reduces, but it is easy for peak displacement to be affected by long-period drifting. In various components, the residual enlargement phenomenon at vertical direction is almost unobvious, thus it is recommended in this article that the effective peak acceleration at vertical direction is preferred to estimate earthquake early warning magnitude. Through adopting Wenchuan strong earthquake record to check the method mentioned in this article, it is found that this method can be used to quickly, stably, and accurately estimate the early warning magnitude of this earthquake, which shows that this method is completely applicable for earthquake early warning.

Earthquake magnitude estimation using the s_c and P_d method for earthquake early warning systems

Earthquake early warning （EEW） systems are one of the most effective ways to reduce earthquake disaster. Earthquake magnitude estimation is one of the most important and also the most difficult parts of the entire EEW system. In this paper, based on 142 earthquake events and 253 seismic records that were recorded by the KiK-net in Japan, and aftershocks of the large Wenchuan earthquake in Sichuan, we obtained earthquake magnitude estimation relationships using the τe and Pa methods. The standard variances of magnitude calculation of these two formulas are ±0.65 and ±0.56, respectively. The Pd value can also be used to estimate the peak ground motion of velocity, then warning information can be released to the public rapidly, according to the estimation results. In order to insure the stability and reliability of magnitude estimation results, we propose a compatibility test according to the natures of these two parameters. The reliability of the early warning information is significantly improved though this test.

Probabilistic earthquake early warning times in Fujian Province

Earthquake early warning （EEW） systems are a new and effective way to mitigate the damage associated with earthquakes. A prototype EEW system is currently being constructed in the Fujian Province, a region along the Southeast coast of China. It is anticipated that the system will be completed in time to be tested at the end of this year （2013）. In order to evaluate how much advanced warning the EEW system will be able to provide different cities in Fujian, we established an EEW information release scheme based on the seismic monitoring stations distributed in the region. Based on this scheme, we selected 71 historical earthquakes. We then obtained the delineation of the region＇s potential seismic source data in order to estimate the highest potential seismic intensities for each city as well as the EEW system warning times. For most of the Fujian Province, EEW alarms would sound several seconds prior to the arrival of the destructive wave. This window of time gives city inhabitants the opportunity to take protective measures before the full intensity of the earthquake strikes.

Comparison of two earthquake early warning location methods

According to earthquake catalog records of Fujian Seismic Network, the Tnow method and the fourstation continuous location method put forward by Jin Xing are inspected by using P-wave arrival information of the first four stations in each earthquake. It shows that the fourstation continuous location method can locate more seismic events than the Tnow method. By analyzing the results, it is concluded that the reason for this is that the Tnow method makes use of information from stations without being triggered, while some stations failed to be reflected in earthquake catalog because of discontinuous records or unclear records of seismic phases. For seismic events whose location results can be given, there is no obvious difference in location results of the two methods and positioning deviation of most seismic events is also not significant. For earthquakes outside the network, the positioning deviation may amplify as the epicentral distance enlarges, which may relate to the situation that the seismic stations are centered on one side of epicenter and the opening angle between seismic stations used for location and epicenter is small.

Progress of the earthquake early warning system in Fujian, China

In this article, we systematically introduce the atest progress of the earthquake early warning （EEW） ;ystem in Fujian, China. We focus on the following key echnologies and methods： continuous earthquake location md its error evaluation; magnitude estimation; reliability udgment of EEW system information; use of doubleparameter principle in EEW system information release hreshold; real-time estimation of seismic intensity and available time for target areas; seismic-monitoring network and data sharing platform; EEW system information ; elease and receiving platform; software test platform; and est results statistical analysis. Based on strong ground notion data received in the mainshock of the Wenchuan earthquake, the EEW system developed by the above algorithm is simulated online, and the results show that the ;ystem can reduce earthquake hazards effectively. In lddition, we analyzed four earthquake cases with magniude greater than 5.5 processed by our EEW system since he online-testing that was started one year ago, and results ndicate that our system can effectively reduce earthquake lazards and have high practical significance.

Evaluation of earthquake signal characteristics for early warning

This paper evaluates different characteristics for earthquake early warning. The scaling relationships between magnitude, epicenter distance and calculated parameters are derived from earthquake event data fi＇om USGS. The standard STA/LTA method is modified by adding two new parameters to eliminate the effects of the spike-type noise and small pulsetype noise ahead of the onset of the P-wave. After the detection of the P-wave, the algorithm extracts 12 kinds of parameters from the first 3 seconds of the P-wave. Then stepwise regression analysis of these parameters is performed to estimate the epicentral distance and magnitude. Six different parameters are selected to estimate the epicentral distance, and the median error for all 419 estimates is 16.5 krn. Four parameters are optimally combined to estimate the magnitude, and the mean error for all events is 0.0 magnitude units, with a standard deviation of 0.5. Finally, based on the estimation results, additional work is proposed to improve the accuracy of the results.

A Study on the Early Warning Time of Strong Earthquakes in Taiwan

The paper collects the records by the Fujian Digital Seismic Network of 40 shallow earthquakes in Taiwan with M_S≥5.0 from 1999 to 2013,analyzes the seismic phase(Pn,Sn phase)characteristics and travel-time rules,determines travel-time models and develops a seismic phase travel-time equation based on the two-step fitting algorithm.With the deduction of processing time and network delay time,this method can provide an accurate estimation of early warning time of Taiwan earthquakes for the Fujian region,and has been officially employed in the earthquake early warning system of Fujian Province.

Preface to the special issue on Earthquake early warning system and rapid seismic instrumental intensity report

In the past several years, from May 12, 2008 Wenchuan Mw8.0 earthquake in China to March 11, 2011 off the Pacific coast of Northeastern Mw9.0 earthquake in Japan, the world witnessed catastrophic disasters caused by destructive earthquakes. The earthquake posed a great threat to the development of society and economy, especially in the developing countries such as China. In order to reduce the losses in peoples life and properties in maximum possibilities, there were a lots of technologies had been researched and developed, among them the earthquake early warning system （EEWS） and rapid seismic instrumental intensity report （RSIIP） are the two of the state-of-the-art technologies for the purpose. They may be used to minimize property damage and loss of life and to aid emergency response after a destructive earthquake.

Recent Developments of Earthquake Early Warning in California,USA

In this paper we outline the science,engineering,and societal considerations of the prototype Earthquake Early Warning System( EEWS) in California and detail the development and testing of methodologies in the last 10 years in America. Also,we give a brief introduction of Earthquake Early Warning( EEW) in China,and based on the summary of EEW in California we make an analysis of the perspectives,misconceptions,and challenges that China may have.

Current Earthquake Early Warning Technology and Its Development in China

Earthquake Early Warning ( EEW) has come to attention,as earthquake prediction is still unreliable. The paper comprehensively illustrates the research status and important issues of EEW from the aspects of concept,composition and method. By analyzing the status of EEW in China,we find that the essential requirements have been met for building earthquake early warning systems in the country in terms of government and social needs, network construction and basic research. The technical difficulties and non-technical challenges in implementing EEW in China are evaluated, and some suggestions are proposed regarding the relevant legal measures,public education and protection against earthquake disasters. so as to bring into full play the role of the EEW system in earthquake disaster prevention and reduction.

Discussion on Earthquake Forecasting and Early Warning

Through analysis of natural and social attributes of earthquake forecasting,the relationship between the natural and social attributes of earthquake forecasting(early warning)has been discussed.Regarding the natural attributes of earthquake forecasting,it only attempts to forecast the magnitude,location and occurrence time of future earthquake based on the analysis of observational data and relevant theories and taking into consideration the present understanding of seismogeny and earthquake generation.It need not consider the consequences an earthquake forecast involves,and its purpose is to check out the level of scientific understanding of earthquakes.In respect of the social aspect of earthquake forecasting,people also focus on the consequence that the forecasting involves,in addition to its natural aspect,such as the uncertainty of earthquake prediction itself,the impact of earthquake prediction,and the earthquake resistant capability of structures(buildings),lifeline works,etc.In a word,it highlights the risk of earthquake forecasting and tries to mitigate the earthquake hazard as much as possible.In this paper,the authors also discuss the scientific and social challenges faced in earthquake prediction and analyze preliminarily the meanings and content of earthquake early warning.

Comparison of Two Earthquake Location Methods for Seismic Early Warning

In this paper,according to the Fujian Seismic Network earthquake catalog records,the T now method and the Four Stations Continuous Location method( hereinafter called FSCL)put forward by Jin Xing are inspected by using P-wave arrival information of the first four stations of each seismic event. Results show that for earthquakes within the network,both methods can obtain similar location results and location deviations are small for the majority of the events. For earthquakes outside the network,the location deviation may be amplified as the epicentral distance increases,owing to the seismic station distribution which spread toward the side of the epicenter and the small opening angle between seismic stations used for locating and epicenter. For the FSCL method,the impacts of the wave velocity on the location results may be significant for earthquakes outside the network.Thus,selecting a velocity model which is similar to the actual structure of the wave velocity will contribute to improving location results of earthquakes. The FSCL method can locate more seismic events than the T now method. It concludes that the T now method makes use of mistake information from some non-triggering stations in earthquake catalog,and some P-wave arrivals are not included in the earthquake catalog due to discontinuous records or unclear records of the seismic phase,which induces incorrect location.

Continuous prediction method of earthquake early warning magnitude for high-speed railway based on support vector machine

Purpose–Using the strong motion data ofK-net in Japan,the continuous magnitude prediction method based on support vector machine(SVM)was studied.Design/methodology/approach–In the range of 0.5–10.0 s after the P-wave arrival,the prediction time window was established at an interval of 0.5 s.12 P-wave characteristic parameters were selected as the model input parameters to construct the earthquake early warning(EEW)magnitude prediction model(SVM-HRM)for high-speed railway based on SVM.Findings–The magnitude prediction results of the SVM-HRM model were compared with the traditional magnitude prediction model and the high-speed railway EEW current norm.Results show that at the 3.0 s time window,themagnitude prediction error of the SVM-HRMmodel is obviously smaller than that of the traditionalτc method and Pd method.The overestimation of small earthquakes is obviously improved,and the construction of the model is not affected by epicenter distance,so it has generalization performance.For earthquake events with themagnitude range of 3–5,the single station realization rate of the SVM-HRMmodel reaches 95%at 0.5 s after the arrival of P-wave,which is better than the first alarm realization rate norm required by“The TestMethod of EEW andMonitoring Systemfor High-Speed Railway.”For earthquake eventswithmagnitudes ranging from3 to 5,5 to 7 and 7 to 8,the single station realization rate of the SVM-HRM model is at 0.5 s,1.5 s and 0.5 s after the P-wave arrival,respectively,which is better than the realization rate norm of multiple stations.Originality/value–At the latest,1.5 s after the P-wave arrival,the SVM-HRM model can issue the first earthquake alarm that meets the norm of magnitude prediction realization rate,which meets the accuracy and continuity requirements of high-speed railway EEW magnitude prediction.

A new early-warning prediction system for monitoring shear force of fault plane in the active fault

The most common method used to describe earthquake activity is based on the changes in physical parameters of the earth＇s surface such as displacement of active fault and seismic wave.However,such approach is not successful in forecasting the movement behaviors of faults.In the present study,a new mechanical model of fault activity,considering the shear strength on the fault plane and the influence of the resistance force,is established based on the occurrence condition of earthquake.A remote real-time monitoring system is correspondingly developed to obtain the changes in mechanical components within fault.Taking into consideration the local geological conditions and the history of fault activity in Zhangjiakou of China,an active fault exposed in the region of Zhangjiakou is selected to be directly monitored by the real-time monitoring technique.A thorough investigation on local fault structures results in the selection of two suitable sites for monitoring potential active tectonic movements of Zhangjiakou fault.Two monitoring curves of shear strength,recorded during a monitoring period of 6 months,turn out to be steady,which indicates that the potential seismic activities hardly occur in the adjacent region in the near future.This monitoring technique can be used for early-warning prediction of the movement of active fault,and can help to further gain an insight into the interaction between fault activity and relevant mechanisms.

Real-time prediction of earthquake potential damage:A case study for the January 8,2022 M_(S) 6.9 Menyuan earthquake in Qinghai,China

It is critical to determine whether a site has potential damage in real-time after an earthquake occurs,which is a challenge in earthquake disaster reduction.Here,we propose a real-time Earthquake Potential Damage predictor(EPDor)based on predicting peak ground velocities(PGVs)of sites.The EPDor is composed of three parts:(1)predicting the magnitude of an earthquake and PGVs of triggered stations based on the machine learning prediction models;(2)predicting the PGVs at distant sites based on the empirical ground motion prediction equation;(3)generating the PGV map through predicting the PGV of each grid point based on an interpolation process of weighted average based on the predicted values in(1)and(2).We apply the EPDor to the 2022 M_(S) 6.9 Menyuan earthquake in Qinghai Province,China to predict its potential damage.Within the initial few seconds after the first station is triggered,the EPDor can determine directly whether there is potential damage for some sites to a certain degree.Hence,we infer that the EPDor has potential application for future earthquakes.Meanwhile,it also has potential in Chinese earthquake early warning system.

基于LSTM网络的单台仪器地震烈度预测模型

烈度是地震预警系统的关键产出.如何实现快速预测目标场址的地震烈度是地震预警方法技术研究中的核心问题.本文提出了一种基于长短时记忆神经网络(Long Short-Term Memory,LSTM)的单台仪器地震烈度的预测模型(LSTM-Ⅰ).该模型以一个台站观测到地震动参数的时间序列特征为输入,实现动态预测该台站可能遭受的最大烈度.选取了日本K-NET台网记录的102次地震的5103条强震加速度记录训练了神经网络,利用89次地震的3781条数据检验了模型的泛化能力.利用准确率、漏报率以及误报率三个评价指标评价了LSTM-Ⅰ模型的性能.结果表明,当采用P波触发后3 s的序列进行预测时,模型出现漏报的概率为46.78%,出现误报的概率为1.25%;当采用P波触发后10 s的序列进行预测时,模型出现漏报的概率大幅降低到17.6%,出现误报的概率降低到1.14%.结果表明LSTM-Ⅰ模型很好把握住了时间序列中蕴含的特征.进一步基于LSTM-Ⅰ模型评估了Ⅵ度下台站所能提供的预警时间.本文模型能够提供的预警时间与P-S波到时差接近,说明LSTM-Ⅰ模型具有较高的时效性.

国家紧急地震信息服务系统的设计、开发及实践

紧急地震信息服务系统是国家地震烈度速报与预警工程五大技术系统的重要组成部分之一,也是对外发布紧急地震信息的关键环节和出口。作为国家地震烈度速报与预警工程“先行先试”单位,四川省地震局基于分布式服务架构开发平台,运用MQTT消息队列等技术,采用JAVA语言完成了四川紧急地震信息服务系统(B系统)的设计与定制开发。目前,该系统已纳入国家地震烈度速报与预警工程紧急地震信息发布的核心业务系统并在全国范围内部署应用,解决了现有紧急地震信息发布系统单一运行的风险,进一步提高了信息发布的稳定性、安全性和时效性。