虚拟地震学家(VS)方法在中国地震台网中的测试和评估
CSTR:
作者:
中图分类号:

P315

基金项目:

地震科技星火计划项目(XH19053)资助


Testing and Evaluation of Virtual Seismologist(VS)Method in China Seismological Network
Author:
  • 摘要
  • | |
  • 访问统计
  • |
  • 参考文献 [54]
  • |
  • 相似文献 [20]
  • | | |
  • 文章评论
    摘要:

    建设中国地震预警系统是国家地震烈度速报与预警工程的主要内容,国内外学者通过对预警系统中确定地震参数的方法研究,发现实时确定准确震级这一问题最具挑战性,亟需一种可用的震级实时测定方法用于建设中国的地震预警系统。本文简要介绍了虚拟地震学家(VS)方法,描述了VS方法在中国地震台网用于实时测定地震参数的软件的实现流程,剖析用该方法实时确定2019年6月17日四川长宁6.0级地震的MVS的过程。通过实时在线测定134个M≥3.0地震的MVS和回放事件波形测定24个M≥5.0地震的MVS,对VS方法进行测试,评估其可用性。结果表明,使用VS方法实时确定的MVS变化平稳,可用性较好。当3个台站的P波信息可用时,第一次测定MVS时偏差δ的平均值为0.32,δ≤0.5的占79%,平均用时为20s。随着时间的推移,更多可用台站及波形的使用可有效提升测定MVS准确度。该方法在中国地震台网的应用具有适用性和可行性,是一种可选的实时确定震级的方法,在地震预警中具有较好的应用潜力。

    Abstract:

    The construction of Chinese earthquake early warning system is the main content of the national earthquake intensity quick report and early warning project. In the study and practice on the methods of determining earthquake parameters in the early warning system, scholars in and abroad found that the problem of the determination of the accurate magnitude in real time is challenging. Therefore, there is an urgent need for available real-time magnitude measurement methods to be used in the construction of Chinese earthquake early warning system. This paper briefly introduces the virtual seismologist(VS)method, describes the implementation process of VS method in the software for real-time determination of seismic parameters in China seismic network, and analyzes the process of using this method to determine the MVS of Sichuan Changning M6.0 earthquake on June 17, 2019. Using the data of China seismic network constructed by the tenth five year plan project, the MVS of 134 earthquakes with M≥3.0 and 24 earthquakes with M≥5.0 are measured by real-time online measurement and playback event waveform. The VS method is tested and its availability is evaluated. The results show that the MVS determined in real time by VS method changes smoothly and has good availability. When the P-wave information of three stations is available, MVS is measured for the first time, the average value of deviationδis 0.32, the number of earthquakes with δ≤0.5 accounted for 79%, and the average time was 20s. As time goes on, the use of more stations and available waveforms can effectively improve the accuracy of MVS measurement. The application of this method in China seismic network is applicability and feasibility. It is an optional method for determining earthquake magnitude in real time, and has good application potential in earthquake early warning.

    参考文献
    [1] 陈锋, 杨建思, 王伟平, 等, 2019. 基于高频GPS峰值地动位移的震级标度探讨. 中国地震, 35 (1): 25~37.
    [2] 胡安冬, 张海明, 2020. 机器学习在地震紧急预警系统震级预估中的应用. 地球物理学报, 63(7): 2617~2626.
    [3] 金星, 马强, 李山有, 2004a. 利用数字强震仪记录实时仿真地动速度. 地震工程与工程振动, 24(1): 49~54.
    [4] 金星, 马强, 李山有, 2004b. 利用数字化速度记录实时仿真位移与加速度时程. 地震工程与工程振动, 24(6): 9~14, 38.
    [5] 金星, 张红才, 李军, 等, 2012. 地震预警震级确定方法研究. 地震学报, 34(5): 593~610.
    [6] 梁姗姗, 雷建设, 徐志国, 等, 2018. 2017年四川九寨沟MS7.0强震的余震重定位及主震震源机制反演. 地球物理学报, 61(5): 2163~2175.
    [7] 刘瑞丰, 高景春, 陈运泰, 等, 2008. 中国数字地震台网的建设与发展. 地震学报, 30(5): 533~539.
    [8] 马强, 2008. 地震预警技术研究及应用. 博士学位论文. 哈尔滨: 中国地震局工程力学研究所.
    [9] 彭朝勇, 杨建思, 2019. 利用P波参数阈值实时估算地震预警潜在破坏区范围. 地震学报, 41(3): 354~365.
    [10] 彭朝勇, 杨建思, 薛兵, 等, 2013. 基于汶川主震及余震的预警参数与震级相关性研究. 地球物理学报, 56(10): 3404~3415.
    [11] 单新建, 屈春燕, 龚文瑜, 等, 2017. 2017年8月8日四川九寨沟7.0级地震InSAR同震形变场及断层滑动分布反演. 地球物理学报, 60(12): 4527~4536.
    [12] 申文豪, 李永生, 焦其松, 等, 2019. 联合强震记录和InSAR/GPS结果的四川九寨沟7.0级地震震源滑动分布反演及其地震学应用. 地球物理学报, 62(1): 115~129.
    [13] 张红才, 金星, 李军, 等, 2012. 地震预警震级计算方法研究综述. 地球物理学进展, 27(2): 464~474.
    [14] 郑绪君, 张勇, 汪荣江, 2017. 采用IDS方法反演强震数据确定2017年8月8日九寨沟地震的破裂过程. 地球物理学报, 60(11): 4421~4430.
    [15] Abercrombie R, Mori J, 1994. Local observations of the onset of a largeearthquake: 28 June 1992 Landers, California. Bull Seismol Soc Am, 84(3): 725~734.
    [16] Abercrombie R E, 2019. Small and large earthquakes can have similar starts. Nature, 573(7772): 42~43.
    [17] Allen RM, 2007. The ElarmS earthquake early warning methodology and application across California. In: Gasparini P, Manfredi G, Zschau J. Earthquake Early Warning Systems. Heidelberg: Springer, 21~43.
    [18] Allen RM, Kanamori H, 2003. The potential for earthquake early warning in Southern California. Science, 300(5620): 786~789.
    [19] ArandaJ M E, Jime'nez A, Ibarrola G, et al, 1995. Mexico City seismic alert system. Seismol Res Lett, 66(6): 42~53.
    [20] BehrY, Clinton J, K?stli P, et al, 2015. Anatomy of an earthquake early warning(EEW)alert: predictingtime delays for an end-to-end EEW system. Seismol Res Lett, 86(3): 830~840.
    [21] BehrY, Clinton J F, CauzziC, et al, 2016. The virtual seismologist in SeisComP3: a new implementation strategy for earthquake early warning algorithms. Seismol Res Lett, 87(2A): 363~373.
    [22] BooreD M, Atkinson G M, 2008. Ground-motion prediction equations for the average horizontal component of PGA, PGV, and 5%-damped PSA at spectral periods between 0.01s and 10.0s. Earthq Spectra, 24(1): 99~138.
    [23] B?se M, Allen R, Brown H, et al, 2014. CISNShakeAlert: an earthquake early warning demonstration systemfor California. In: Wenzel F, Zschau J. Early Warning for Geological Disasters. Heidelberg: Springer, 49~69.
    [24] ChenDY, HsiaoN C, Wu Y M, 2015. The Earthworm basedearthquake alarm reporting system in Taiwan. Bull Seismol Soc Am, 105(2A): 568~579.
    [25] Cua, 2005. Creating the Virtual Seismologist: Developments in Ground Motion Characterization and Seismic Early Warning. Ph. D. Thesis, California Institute of Technology.
    [26] Cua G, HeatonT, 2007. The Virtual Seismologist(VS)method: a Bayesian approach to earthquake early warning. In: Gasparini P, Manfredi G, Zschau J. Earthquake Early Warning Systems. Heidelberg: Springer, 97~132.
    [27] Cua G, Fischer M, Heaton T, et al, 2009. Real-time performance of the virtual seismologist earthquake early warning algorithm in Southern California. Seismol Res Lett, 80(5): 740~747.
    [28] DanréP, YinJX, Lipovsky B P, et al, 2019. Earthquakes within earthquakes: patterns inrupture complexity. Geophys Res Lett, 46(13): 7352~7360.
    [29] Horiuchi S, Negishi H, Abe K, et al, 2005. An automatic processingsystem for broadcasting earthquake alarms. Bull Seismol Soc Am, 95(2): 708~718.
    [30] Hoshiba M, Kamigaichi O, Saito M, et al, 2008. Earthquake early warning starts nationwide in Japan. EOS, Trans Amer Geophys Union, 89(8): 73~74.
    [31] Hunter J D, 2007. Matplotlib: a 2D graphics environment. Comput Sci Eng, 9(3): 90~95.
    [32] Hutchison AA, B?se M, Manighetti I, 2020. Improving earlyestimates of large earthquake's finalfault lengths and magnitudesleveraging source fault structuralmaturity information. Geophys Res Lett, 47(14): e2020GL087539.
    [33] Ide S, 2019. Frequent observations of identical onsets of large and small earthquakes. Nature, 573(7772): 112~116.
    [34] KanamoriH, 2005. Real-time seismology and earthquake damage mitigation. Annu Rev Earth Planet Sci, 33: 195~214.
    [35] Lancieri M, Zollo A, 2008. Abayesian approach to the real-time estimation of magnitude from the early P and Swave displacement peaks. J Geophys Res: Solid Earth, 113(B12): B12302.
    [36] LockmanA B, Allen R M, 2005. Single-station earthquake characterization for early warning. Bull Seismol Soc Am, 95(6): 2029~2039.
    [37] Mǎrmureanu A, Ionescu C, CioflanC O, 2011. Advanced real-timeacquisition of the Vrancea earthquake early warning system. Soil Dyn Earthq Eng, 31(2): 163~169.
    [38] Meier MA, Ampuero JP, Heaton TH, 2017. The hidden simplicity of subduction megathrust earthquakes. Science, 357(6357): 1277~1281.
    [39] Nakamura Y, Saita J, 2007. UrEDAS, the earthquake warning system: today and tomorrow. In: Gasparini P, Manfredi G, Zschau J. Earthquake Early Warning Systems. Heidelberg: Springer, 249~281.
    [40] OdakaT, Ashiya K, TsukadaS Y, et al, 2003. A new method of quickly estimating epicentral distance and magnitude from a single seismic record. Bull Seismol Soc Am, 93(1): 526~532.
    [41] Olson E L, Allen R M, 2005. The deterministic nature of earthquake rupture. Nature, 438(7065): 212~215.
    [42] PengCY, YangJ S, Zheng Y, et al, 2017. New τc regression relationship derived from all P wave time windows for rapid magnitude estimation. Geophys Res Lett, 44(4): 1724~1731.
    [43] Peng H S, Wu Z L, WuY M, et al, 2011. Developing a prototype earthquake early warning system in theBeijing capital region. Seismol Res Lett, 82(3): 394~403.
    [44] Satriano C, Lomax A, Zollo A, 2008. Real-time evolutionary earthquake location for seismic early warning. Bull Seismol Soc Am, 98(3): 1482~1494.
    [45] Wang Y, Li SY, Song JD, 2020. Threshold-based evolutionary magnitude estimation for an earthquake early warning system in the Sichuan-Yunnan region, China. Sci Rep, 10(1): 21055.
    [46] Wessel P, Luis J F, Uieda L, et al, 2019. The generic mapping tools version 6. Geochem, Geophys, Geosyst, 20(11): 5556~5564.
    [47] WolfeC J, 2006. On the properties of predominant-period estimators for earthquake early warning. Bull Seismol Soc Am, 96(5): 1961~1965.
    [48] Wu Y M, Kanamori H, 2005. Experiment on an onsite early warning method for the Taiwan early warning system. Bull Seismol Soc Am, 95(1): 347~353.
    [49] Wu YM, KanamoriH, 2008. Development of an earthquake early warning system using real-time strong motion signals. Sensors, 8(1): 1~9.
    [50] Wu Y M, Zhao L, 2006. Magnitude estimation using the first three seconds P-wave amplitude in earthquake early warning. Geophys Res Lett, 33(16): L16312.
    [51] YamadaM, Mori J, 2009. Usingτc to estimate magnitude for earthquake early warning and effects of near-field terms. J Geophys Res: Solid Earth, 114(B5): B05301.
    [52] Yamamoto S, Rydelek P, Horiuchi D, et al, 2008. On the estimation of seismic intensity in earthquake early warning systems. Geophys Res Lett, 35(7): L07302.
    [53] ZhuJB, Li SY, Song JD, et al, 2021. Magnitude estimation for earthquake early warning using a deep convolutional neural network. Front Earth Sci, 9: 653226.
    [54] Zollo A, Colombelli S, Elia L, et al, 2014. An integrated regionalandon-site earthquake early warning system for southern Italy: concepts, methodologiesand performances. In: Wenzel F, Zschau J. Early Warning for Geological Disasters. Heidelberg: Springer, 117~137.
    引证文献
    网友评论
    网友评论
    分享到微博
    发 布
引用本文

孙丽,梁建宏,徐志国,刘杰.虚拟地震学家(VS)方法在中国地震台网中的测试和评估[J].中国地震,2021,37(4):843-856

复制
分享
文章指标
  • 点击次数:592
  • 下载次数: 1108
  • HTML阅读次数: 629
  • 引用次数: 0
历史
  • 收稿日期:2020-12-25
  • 最后修改日期:2021-07-25
  • 在线发布日期: 2022-01-24
文章二维码
您是第2857384位访问者
中国地震 ® 2025 版权所有
技术支持:北京勤云科技发展有限公司
请使用 Firefox、Chrome、IE10、IE11、360极速模式、搜狗极速模式、QQ极速模式等浏览器,其他浏览器不建议使用!