Key technologies of earthquake early warning system for China’s high-speed railway

Purpose–The purpose of this study is to introduce the top-level design ideas and the overall architecture of earthquake early-warning system for high speed railways in China,which is based on P-wave earthquake early-warning and multiple ways of rapid treatment.Design/methodology/approach–The paper describes the key technologies that are involved in the development of the system,such as P-wave identification and earthquake early-warning,multi-source seismic information fusion and earthquake emergency treatment technologies.The paper also presents the test results of the system,which show that it has complete functions and its major performance indicators meet the design requirements.Findings–The study demonstrates that the high speed railways earthquake early-warning system serves as an important technical tool for high speed railways to cope with the threat of earthquake to the operation safety.The key technical indicators of the system have excellent performance:The first report time of the P-wave is less than three seconds.From the first arrival of P-wave to the beginning of train braking,the total delay of onboard emergency treatment is 3.63 seconds under 95%probability.The average total delay for power failures triggered by substations is 3.3 seconds.Originality/value–The paper provides a valuable reference for the research and development of earthquake early-warning system for high speed railways in other countries and regions.It also contributes to the earthquake prevention and disaster reduction efforts.

The medium- and short-term prediction methods of strong earthquakes based on neural network

The field of neural network has found solid application in the past ten years and the field itself is still developing rapidly. Neural network is composed of many simple elements operating in parallel. A neural network can be trained to perform a particular mapping and this is the basis of its application to practical problems. In this paper, new methods for predicting the strong earthquakes are presented based on neural network. Neural network learns from existing earthquake sequences or earthquake precursors how to make medium and short term prediction of strong earthquakes. This paper describes two neural network prediction models. One is the model based on earthquake evolution sequences, which is applied to the modeling of the magnitude evolution sequences in the Mainland of China, the other is based on earthquake precursors, which is applied to the modeling of the occurrence time of strong earthquakes in North China. Test results show that the prediction methods based on neural networks are efficient, and convenient. They would find more application in the future.

Analysis of coseismic effect on temperature in the Three Gorges well network

Through the Three Gorges well network, we examine different coseismic changes in water temperature caused by local earthquakes since 2008, and offer a mechanistic explanation.The relations between the coseismic changes in water temperature and the parameters of distant and local earthquakes are deduced.

The application of neural networks to comprehensive prediction by seismology prediction method

BP neural networks is used to mid-term earthquake prediction in this paper. Some usual prediction parameters of seismology are used as the import units of neural networks. And the export units of neural networks is called as the character parameter W_0 describing enhancement of seismicity. We applied this method to space scanning of North China. The result shows that the mid-term anomalous zone of W_0-value usually appeared obviously around the future epicenter 1~3 years before earthquake. It is effective to mid-term prediction.

ThesourceparametersandsourcemechanismfortheJanuary5,1998JingyangearthquakewithMS=4.8

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Gravity variation before the Akto Ms6.7 earthquake, Xinjiang

The relationship between gravity variation and the Akto Ms6.7 earthquake on November 11, 2016, was studied by use of mobile gravity observation data from the China continental structural environmental monitoring network. The result revealed that before the Akto earthquake, a high positive gravity variation was observed in the Pamir tectonic knots region （within a maximum magnitude of approximately ＋80 microgal）, which was consistent with the existing knowledge of gravity abnormality and the locations of strong earthquakes. In view of the recent strong seismic activities in the Pamir tectonic knots region, as well as the strong upward crust movement and compressive strain, it is believed that gravity change in the Pamir tectonic knots region reflects the recent strong seismic activities and crust movement.

Study of calibration function for surface wave magnitude of DK1 seismographs

The calibration function for surface wave magnitude of DK1 seismograph is deduced by using least squares method of error theory based on the records of DK1 seismograph from 1987 to 1993 and the MS magnitude defined by International Seismological Center (ISC) as basic standard, which takes the form σ DK1 ( Δ )=(1.66±0.034)lg( Δ )+(3.2±0.11) (10° ≤ Δ ≤ 130°, T =8～20 s) From its physical essence, the calibration function for estimating surface wave magnitude by DK1 seismograph can be also deduced by means of the wave propagation theory in layer media, if the dispersion of seismic wave, absorption of media and the effect of the linear filter consisting of earth media and DK1 seismographs on the amplitudes and periods of surface wave are taken into account and it is also assumed that the maximum amplitude is response to Airy?s phase, which takes the form σ DK1 ( Δ )=1.661lg( Δ )+3.3 (10°≤ Δ ≤170°) No correction value is needed when using the calibration function deduced in this paper. Examination on surface wave magnitude of 315 events by 36 prompt earthquake stations and DK1 prompt network shows there exits no systematical error between surface magnitudes of MS (DK1) and MS (ISC) given by ISC and the average error is approximately zero.