«上一篇/Previous Article|本期目录/Table of Contents|下一篇/Next Article»

HTML)

《地震研究》[ISSN:1000-0666/CN:53-1062/P]

卷:: 44
期数:: 2021年02期

页码:: 208-215

栏目:

出版日期:: 2021-06-30

文章信息/Info

Title:: The Applicability Analysis of STA/LTA Algorithm for MEMS Seismic Intensity Instruments

作者:: 钟媛; 胡星星^; 滕云田; 陈波; 何朝博; 沈晓宇; (中国地震局地球物理研究所,北京 100081)

Author(s):: ZHONG Yuan; HU Xingxing; TENG Yuntian; CHEN Bo; HE Zhaobo; SHEN Xiaoyu; (Institute of Geophysics,China Earthquake Administration,Beijing 100081,China)

关键词:: MEMS烈度计; STA/LTA; 拾取; 运算时间

Keywords:: MEMS seismic intensity instruments; STA/LTA; picking; computing-time

分类号:: P315.61

DOI:: -

摘要:: 为解决用于高密度布设的低成本MEMS烈度计集成软、硬件资源有限,且难以嵌入较为复杂算法的这一问题,基于Matlab的仿真计算,通过讨论在不同特征函数、时窗长度和短窗位置下STA/LTA值的变化趋势、拾取效果和运算时间,以选取能提高算法灵敏性、改善地震事件拾取效果和提高算法计算效率的参数,并将改进的STA/LTA算法应用于实际地震数据处理。结果表明:不同的特征函数对事件拾取率、拾取时间偏差和算法运算时间影响不同; 长短时窗长度相差越大,STA/LTA值的变化越明显; 时窗越长,算法运算时间越长; 短窗置后可以增大STA/LTA值的变化幅度、减少算法计算量,改善算法拾取时间。改进的STA/LTA算法拾取效果更好,计算效率更高,占用内存资源更小,更适用于集成资源有限的MEMS烈度计。

Abstract:: Though a number of forecasting techniques are used for the detection of seismic events,each of them has its own pros and cons.Since the MEMS seismic intensity instrument which features low cost and follows the principle of high-density deployment has limitation in integrating software and hardware resources,it is difficult to embed some more complex algorithms.Based on the simulation calculation of Matlab,this paper aims to select the parameters that can improve algorithmic sensitivity,picking-effect of seismic events and calculation efficiency according to different factors of STA/LTA algorithm including variation tendency of the STA/LTA value,picking-effect and computing-time in various situations such as different characteristic functions,different window lengths and different short-window locations.And the improvement of STA/LTA algorithm is applied to practical seismic-data processing.The final analysis suggests that diverse characteristic functions have corresponding effects on facets like event picking-rate,picking-time deviation and computing-time of algorithms; the greater difference between the lengths of long-time windows and short-time ones,the more obviously the values of STA/LTA tend to change; the computing-time of algorithm depends on the length of the window(positive correlation); putting short windows after long ones can intensify variation range of STA/LTA values,reduce calculation amount and improve the picking time.In conclusion,the improved STA/LTA algorithm can improve the picking-effect,calculation efficiency and use less memory resources,so it is more suitable for MEMS intensity instruments with limited integrated resources.

参考文献/References:

蔡莉,占伟伟,王秀,等.2014.基于微机电加速度计的地震烈度计[J].地震地磁观测与研究,35(1/2):240-246.
段建华,程建远,王云宏,等.2015.基于STA/LTA方法的微地震事件自动识别技术[J].煤田地质与勘探,43(1):76-80+85.
胡星星,高孟潭,滕云田,等.2015.地震动参数速报仪的研制[J].地球物理学报,58(3):1024-1034.
胡星星,滕云田,谢凡,等.2013.基于MEMS传感器的速度型地震计技术研究[J].地球物理学进展,28(1):515-522.
蒋策,吴建平,房立华.2018.地震检测与震相自动拾取研究[J].地震学报,40(1):45-57.
李昌珑.2013.基于MEMS加速度传感器的地震烈度监测技术研究与实现[D].北京:中国地震局地震研究所.
林建民,杨微,陈蒙,等.2012.偏振分析在地震信号检测中的应用[J].中国地震,28(2):133-143.
刘晗,张建中.2014.微震信号自动检测的STA/LTA算法及其改进分析[J].地球物理学进展,29(4):1708-1714.
刘劲松,王赟,姚振兴.2013.微地震信号到时自动拾取方法[J].地球物理学报,56(5):1660-1666.
刘希强,周蕙兰,郑治真,等.1998.基于小波变换的信号突变检测、滤波和瞬态谱研究[J].国际地震动态,(1):1-9.
马强,金星,李山有,等.2013.用于地震预警的P波震相到时自动拾取[J].地球物理学报,56(7):2313-2321.
牟培杰,王润秋,李彦鹏,等.2012.微地震事件的自动检测研究[C]//中国地球物理学会第二十八届年会论文集.北京:中国地球物理学会.
滕云田,王喜珍,王晓美,等.2006.用B-样条双正交小波拾取P波到时[J].地震学报,28(3):329-333.
王浩,丁炜.2013.MEMS加速度计与传统地震加速度计的比较研究[J].大地测量与地球动力学,33(S2):93-95.
王建军,吴荣辉,何加勇.2009.基于IPv6和无线网络的地震烈度计开发[J].现代电子技术,32(1):23-25,29.
王喜珍.2004.小波变换在地震数据压缩和震相到时拾取中的应用研究[D].北京:中国地震局地球物理研究所.
吴治涛,李仕雄.2010.STA/LTA算法拾取微地震事件P波到时对比研究[J].地球物理学进展,25(5):1577-1582.
余建华,李丹丹,韩国栋.2011.特征函数响应特性分析及STA/LTA方法的改进[J].国外电子测量技术,30(7):17-19,23,27.
张海涛,阎贵平.2003.MEMS加速度传感器的原理及分析[J].电子工艺技术,(6):260-262,265.
赵岑.2013.P波预警中的震级预测和PGA、PGV估算研究[D].成都:西南交通大学.
赵大鹏,刘希强,李红,等.2012.峰度和AIC方法在区域地震事件和直达P波初动自动识别方面的应用[J].地震研究,35(2):220-225.
Akaike H.1973.Information theory as an extension of the maximum likelihood principle[C]// Proceedings in second international symposium on information theory.Budapest Akademiai Kiado,267-281.
Allen R E.1978.Automatic earthquake recognition and timing form single traces[J].Bulletin of the Seismological Research of America,68(5):1521-1532.
Bai C Y.2000.Automatic phase-detection and identification by full Use of a single three-component broadband seismogram[J].Bulletin of the Seismological Society of America,90(1):187-198.
Chen Y N,Li J,Wang Z J,et al.2020.Quick phase identification for dense seismic array with aid from long term phase records of co-located sparse permanent stations[J].Earthquake Research in China,34(3):328-342.
Jubran A,David E.2012.Adaptive microseismic event detection and automatic time picking[C].Geo Convention 2012:Vision,1-5.
Kwon J,Heo T,Kim J K,et al.2018.A new P-wave detector via moving empirical cumulative distribution function[C].Bulletin of the Seismological Society of America,108(4):2080-2089.
Massan,Ferrettig,Spallarossad,et al.2006.Improving automatic location procedure by waveform similarity analysis:An application in the South Western Alps(Italy)[J].Play Earth Planet Interiors,154:18-29.
Peng C,Chen Y,Chen Q,et al.2017.A new type of tri-axial accelerometers with high dynamic range MEMS for earthquake early warning[J].Computers & Geosciences,100(C):179-187.
Peng C,Jiang P,Chen Q,et al.2019.Performance evaluation of a dense MEMS-based seismic sensor array deployed in the sichuan-Yunnan border region for earthquake early warning[J].Micromachines,10(11):735.
Ruud B O,Huserbye E S.1992.A new three-component detector and automatic single-station bulletin production[J].Bulletin of the Seismological Research of America,82(1):221-237.
Sleeman R,Eck T V.1999.Robust automatic P-phase picking:an on-line implementation in the analysis of broadband seismogram recordings[J].Physics of the Earth and Planetary Interiors,113(1-4):265-275.
Stevenson R.1976.Microearthquakes at Flathead Lake,Montana:A study using automatic earthquake processing[J].Bulletin of the Seismological Research of America,66(5):61-79.
Takanami T,Kitagawa G.1993.Multivariate time-series model to estimate the arrival times of S-waves[J].Pergamon,19(2):295-301.
Xu Z,Wang T,Xu S H,et al.2019.Active source seismic identification and automatic picking of the P-wave first arrival using a convolutional neural network[J].Earthquake Research in China,33(2):288-304.

备注/Memo

备注/Memo:: 收稿日期:2021-01-19
基金项目:国家重点研发计划(2019YFC1511001)和中国地震局地球物理研究所基本科研业务费专项(DQJB19A0133)联合资助.

更新日期/Last Update: 2021-06-30

《地震研究》[ISSN:1000-0666/CN:53-1062/P]

文章信息/Info

参考文献/References:

备注/Memo

常用功能

导航/Navigate

工具/Tools

统计/Statistics