Rapid report of the December 18,2023 M_(S)6.2 Jishishan earthquake,Gansu,China

On December 18,2023,the Jishishan area in Gansu Province was jolted by a M_(S) 6.2 earthquake,which is the most powerful seismic event that occurred throughout the year in China.The earthquake occurred along the NWtrending Lajishan fault(LJSF),a large tectonic transformation zone.After this event,China Earthquake Networks Center(CENC)has timely published several reports about seismic sources for emergency responses.The earthquake early warning system issued the first alert 4.9 s after the earthquake occurrence,providing prompt notification that effectively mitigated panics,injuries,and deaths of residents.The near real-time focal mechanism solution indicates that this earthquake is associated with a thrust fault.The distribution of aftershocks,the rupture process,and the recorded amplitudes from seismic monitoring and GNSS stations,all suggest that the mainshock rupture predominately propagates to the northwest direction.The duration of the rupture process is~12 s,and the largest slip is located at approximately 6.3 km to the NNW from the epicenter,with a peak slip of 0.12 m at~8 km depth.Seismic station N0028 recorded the highest instrumental intensity,which is 9.4 on the Mercalli scale.The estimated intensity map shows a seismic intensity reaching up to IX near the rupture area,consistent with field survey results.The aftershocks(up to December 22,2023)are mostly distributed in the northwest direction within~20 km of the epicenter.This earthquake caused serious casualties and house collapses,which requires further investigations into the impact of this earthquake.

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.

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.

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.

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.

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.

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.

Analysis the First Arrival of P-Wave of Ina-TEWS and CTBT Stations to Support Earthquake Early Warning

The authors make the analysis of first arrivals of the P-wave from Ina-TEWS （Indonesian tsunami early warning system） and CTBT （comprehensive nuclear-test-band treaty） stations. These are used for earthquake early warning, magnitude determination and potential earthquake hazard mitigation based on seismogram acceleration. This research is focused on the study of energy duration of high frequency, and the maximum displacement of P-waves by observing broadband seismograms. The further analysis consists of deconvolution, integration or defferentiation, recursive filtering for data restitution, and applying a Butterworth filter of second order. The Butterworth filter uses high frequency 0.075 Hz to cut the effect of drift, and band-pass frequency 2-4 Hz for use in magnitude calculation. The authors choose potentially damaging earthquakes to be greater than Mw 〉 6.0. Based on the trigger on the three seconds the first arrival P-wave, the dominant period （Td） and amplitude displacement （Pd） was calculated by using data CISI （Indonesian CI Sompet） seismological station, Garut （west Java） and tested for data CTBT, LEM bang, Bandung （LEM station）. This research resulted determination of the P-wave arrival time accurately using integrated skewness and kurtosis. Performance data from the CTBT stations is very high. Signal to noise ratio 〉1,000 after passing through the filter. Such riset conducted to find out a rapid magnitude estimations from predominant frequency of displacement are： log Td = 0.2406 M- 1.3665 （R = 0.73） or M = 4.156 log Td ＋ 5.6797. Relationship of Pd, magnitude moment, Mw and hypocentre, R are log Pd = -4.684 ＋ 0.815 Mw - 1.36 log R. For relation of PGA （peak ground acceleration） and amplitude displacement are log PGA = 1.117 log Pd ＋ 0.728 （R = 0.91）. Furthermore, this formula can be used to support earthquake early warning in west of Java.

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.

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.

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.

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.

Decision-making method for high-speed rail early warning system in complex earthquake situations

To address the shortcomings in decision-making methods for ground motion threshold warning models in high-speed rail earthquake early warning systems(HSREEWs),we propose a dual judgement method and corresponding early warning process for earthquake early warning decisions based on joint peak ground acceleration(PGA)and complex earthquake environmental risk evaluation(ERE)values.First,we analyse the characteristics of four complex earthquake environments based on the characteristics of high-speed rail(HSR)operating environments.Second,we establish an earthquake environmental risk evaluation index system and propose an adversarial interpretive structure modelling method-based complex earthquake situation evaluation model(AISM-based ESEM).The AISM method firstly evaluates the proximity by the TOPSIS(technique for order preference by similarity to an ideal solution)method,then effectively rank targets with fuzzy attributes through opposite hierarchical extraction rules without sacrificing system functionality.Since PGA can reflect the current size of earthquake energy,combining PGA thresholds with ESEM-derived values of ERE can effectively determine the risk status of each train and make decisions on the most appropriate alarm form and control measures for that status.Finally,case analysis results under the background of Wenchuan Earthquake show that the new early warning decisionmaking method accurately assesses environmental risks in affected areas and provides corresponding warning levels as a supplement to existing HSREEWs warning models.

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.

The Application of China's High Speed Railway Earthquake Early Warning System in Jakarta-Bandung HSR

Earthquake has a significant impact on operation safety of the high speed railway,and for Jakarta-Bandung High Speed Railway(HSR)in Indonesia where it is earthquake-prone,it is necessary to establish an earthquake early warning system to strengthen its earthquake resistance.Based on the principle and technical characteristics of China's high speed railway earthquake early warning system and combining the actual situations of Jakarta-Bandung HSR in Indonesia,this paper describes how to implement China's high speed railway earthquake early warning system in Jakarta-Bandung HSR.It focuses on optimizations in environmental adaptation design and seismic network interface design,earthquake attenuation model parameter adjustment and terminal software interface adjustment,so as to make the system better suit the local situations,and meet operation requirements and guarantee safe operation of Jakarta-Bandung HSR.

Toward earthquake early warning:A convolutional neural network for rapid earthquake magnitude estimation

Earthquake early warning(EEW)is one of the important tools to reduce the hazard of earthquakes.In contemporary seismology,EEW is typically transformed into a fast classification of earthquake magnitude,i.e.,large magnitude earthquakes that require warning are in the positive category and vice versa in the negative category.However,the current standard information signal processing routines for magnitude fast classification are time-consuming and vulnerable to data imbalance.Therefore,in this study,Deep Learning(DL)algorithms are introduced to assist with EEW.For the three-component seismic waveform record of 7 s obtained from the China Earthquake Network Center(CENC),this paper proposes a DL model(EEWMagNet),which accomplishes the extraction of spatial and temporal features through DenseBlock with Bottleneck and Multi-Head Attention.Extensive experiments on Chinese field data demonstrate that the proposed model performs well in the fast classification of magnitude.Moreover,the comparison experiments demonstrate that the epicenter distance information is indispensable,and the normalization has a negative effect on the model to capture accurate amplitude information.