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Article(id=1322506469002564066, tenantId=1146029695717560320, journalId=1281212831689347082, issueId=1322506335908901129, articleNumber=null, orderNo=null, doi=10.14075/j.jgg.2024.09.432, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1726675200000, receivedDateStr=2024-09-19, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793239713191, onlineDateStr=2026-10-29, pubDate=1760457600000, pubDateStr=2025-10-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793239713191, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793239713191, creator=13701087609, updateTime=1793239713191, updator=13701087609, issue=Issue{id=1322506335908901129, tenantId=1146029695717560320, journalId=1281212831689347082, year='2025', volume='45', issue='10', pageStart='991', pageEnd='1100', issueExtLink='null', onlineDate='null', pubDate='1760457600000', pubDateStr='2025-10-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793239681460, creator='13701087609', updateTime=1793244369709, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322525999896576487, tenantId=1146029695717560320, journalId=1281212831689347082, issueId=1322506335908901129, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322525999896576488, tenantId=1146029695717560320, journalId=1281212831689347082, issueId=1322506335908901129, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1085, endPage=1092, ext={EN=ArticleExt(id=1322506470281826787, articleId=1322506469002564066, tenantId=1146029695717560320, journalId=1281212831689347082, language=EN, title=Evaluation of Monitoring and Warning Capability of Yunnan Province Seismic Network, columnId=null, journalTitle=Journal of Geodesy and Geodynamics, columnName=null, runingTitle=null, highlight=null, articleAbstract=

This study aims to examine the monitoring and early warning abilities of the Yunnan seismic station network using the 72 h continuous waveform recordings recorded at 2 060 stations. The evaluation index used was the maximum probability peak displacement(PGD) of background noise at each station. The findings revealed that the average monitoring capacity of the Yunnan seismic network raised to ML1.0, while the smallest earthquake that could be monitored was ML0.1, and the monitoring capacity was comparatively weak in the central and northwestern Yunnan area. Moreover, the average values of the minimum magnitude of earthquake early warning, the warning time after the triggering of the first station, and the warning time after the earthquake were ML2.2, 4.0 s, and 6.1 s, respectively. Furthermore, the planning layout of the Yunnan seismic station network fulfilled the criteria for the initial establishment of earthquake early warning.

, authors=Fengying LI1, Yaji XU1, Tianji ZHANG1, Yuqin WU1, Wenjie DANG1, Ying CAO1, authorsList=Fengying LI, Yaji XU, Tianji ZHANG, Yuqin WU, Wenjie DANG, Ying CAO, authorCompany=null, correspAuthors=null, authorNote=

LI Fengying, assistant engineer, majors in seismic observation, E-mail:

, correspAuthorsNote=null, copyrightStatement=All rights reserved. Unauthorized reproduction is prohibited., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322506472886489594, articleId=1322506469002564066, tenantId=1146029695717560320, journalId=1281212831689347082, language=CN, title=云南地震台网监测与预警能力评估分析, columnId=1322506256904994864, journalTitle=大地测量与地球动力学, columnName=观测技术, runingTitle=null, highlight=null, articleAbstract=

利用云南地震台网2 060个站点记录到的72 h连续波形数据，将各台站背景噪声最大概率峰值位移(PGD)作为评估指标，对云南地震台网监测与预警能力进行评估分析。结果显示，云南地震台网平均监测能力达ML1.0，最小可以监测到ML0.1地震，在滇中、滇西北地区监测能力相对薄弱；速度计、加速度计、烈度仪三类传感器融合后的预警最小震级、首台触发和震后预警首报用时平均值分别为ML2.2、4.0 s、6.1 s。云南地震台网总体规划布局满足地震预警初期设置要求。

, authors=李凤英1, 许亚吉1, 张天继1, 吴宇琴1, 党文杰1, 曹颖1, authorsList=李凤英, 许亚吉, 张天继, 吴宇琴, 党文杰, 曹颖, authorCompany=null, correspAuthors=null, authorNote=

李凤英，助理工程师，主要从事地震观测技术研究，E-mail：

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LI Fengying, assistant engineer, majors in seismic observation, E-mail:

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LI Fengying, assistant engineer, majors in seismic observation, E-mail:

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李凤英，助理工程师，主要从事地震观测技术研究，E-mail：

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李凤英，助理工程师，主要从事地震观测技术研究，E-mail：

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F1：怒江断裂；F2：澜沧江断裂；F3：红河断裂；F4：小江断裂；F5：金沙江断裂

, figureFileSmall=Y1+k6eDEkqhNPBTG882PDQ==, figureFileBig=Q5/JUisKSKYgSHnKDTs8Uw==, tableContent=null), ArticleFig(id=1322506476975936038, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506469002564066, language=EN, label=Fig. 2, caption=Distribution of stations of Yunnan seismic network, figureFileSmall=rS/dAEMsfXztzhYATjm8bw==, figureFileBig=uorwui5c/iAdLetxUj25/w==, tableContent=null), ArticleFig(id=1322506477047239207, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506469002564066, language=CN, label=图2, caption=云南地震台网台站分布, figureFileSmall=rS/dAEMsfXztzhYATjm8bw==, figureFileBig=uorwui5c/iAdLetxUj25/w==, tableContent=null), ArticleFig(id=1322506477114348072, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506469002564066, language=EN, label=Fig. 3, caption=Cauge function of Yunnan seismic network, figureFileSmall=ASeW2wbGlK8QB4CX0gsEgg==, figureFileBig=3dc77cZqGwwCYQkTubIEFg==, tableContent=null), ArticleFig(id=1322506477177262633, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506469002564066, language=CN, label=图3, caption=云南地震台网量规函数, figureFileSmall=ASeW2wbGlK8QB4CX0gsEgg==, figureFileBig=3dc77cZqGwwCYQkTubIEFg==, tableContent=null), ArticleFig(id=1322506477235982890, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506469002564066, language=EN, label=Fig. 4, caption=The scatter plots the distribution of background noise PDG of three types of sensors, figureFileSmall=M3XBpHDbkC0ANp4TizUJ5A==, figureFileBig=rCOy0Xh+wO4qJfdX5Iw9sA==, tableContent=null), ArticleFig(id=1322506477315674667, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506469002564066, language=CN, label=图4, caption=三类传感器的背景噪声PGD值散点分布, figureFileSmall=M3XBpHDbkC0ANp4TizUJ5A==, figureFileBig=rCOy0Xh+wO4qJfdX5Iw9sA==, tableContent=null), ArticleFig(id=1322506477382783532, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506469002564066, language=EN, label=Fig. 5, caption=The statistics histogram of background noise PDG of three types of sensors, figureFileSmall=nfTzkSyb/RuIBfPoOIwH+A==, figureFileBig=Q+jbswWmfUs0jPBUlUNxBw==, tableContent=null), ArticleFig(id=1322506477445698093, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506469002564066, language=CN, label=图5, caption=三类传感器的背景噪声PGD值统计直方图, figureFileSmall=nfTzkSyb/RuIBfPoOIwH+A==, figureFileBig=Q+jbswWmfUs0jPBUlUNxBw==, tableContent=null), ArticleFig(id=1322506479110836784, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506469002564066, language=EN, label=Fig. 6, caption=The cumulative probability distribution of background noise PDG of three types of sensors, figureFileSmall=nEH+njiCg9n48lIT39/XIQ==, figureFileBig=lyjJ5RQ6bfBSYoG5uWBNCg==, tableContent=null), ArticleFig(id=1322506479266026033, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506469002564066, language=CN, label=图6, caption=三类传感器的背景噪声PGD值概率分布直方图, figureFileSmall=nEH+njiCg9n48lIT39/XIQ==, figureFileBig=lyjJ5RQ6bfBSYoG5uWBNCg==, tableContent=null), ArticleFig(id=1322506479362495026, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506469002564066, language=EN, label=Fig. 7, caption=Distribution of seismic monitoring capacity of single network and fusion networks, figureFileSmall=yAJ9Laocfsu0yFabYaSGNw==, figureFileBig=g5ZiYVUhXUPs4GYjsJBbWw==, tableContent=null), ArticleFig(id=1322506479484129843, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506469002564066, language=CN, label=图7, caption=单网和多网融合地震监测能力空间分布

底图使用审图号GS(2024)0650号，无修改

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底图使用审图号GS(2024)0650号，无修改

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底图使用审图号GS(2024)0650号，无修改

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The seismic monitoring capacity of single network and fusion networks

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监测能力ML 类型
单网 多网
测震 强震 烈度 测震强震 强震烈度 三网融合
传统 预警 传统 预警 传统 预警 传统 预警 传统 预警 传统 预警
平均值 1.0 1.2 2.0 2.3 3.4 3.6 1.0 1.6 2.0 2.9 1.0 2.2
最小值 0.1 0.3 1.3 1.5 3.1 3.3 0.1 0.1 1.3 1.3 0.1 0.1
最大值 2.0 2.3 2.8 3.4 4.5 4.7 2.0 3.3 2.8 4.3 2.0 4.1
累计概率15% 0.7 0.9 1.8 2.0 3.3 3.5 0.7 0.9 1.8 2.0 0.7 1.2
累计概率55% 1.0 1.2 2.0 2.3 3.4 3.6 1.0 1.7 2.0 3.2 1.0 2.2
累计概率95% 1.5 1.7 2.5 2.9 3.8 4.0 1.5 2.6 2.5 3.5 1.5 3.4
), ArticleFig(id=1322506480251687484, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506469002564066, language=CN, label=表1, caption=

单网和多网融合地震监测能力汇总

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监测能力ML 类型
单网 多网
测震 强震 烈度 测震强震 强震烈度 三网融合
传统 预警 传统 预警 传统 预警 传统 预警 传统 预警 传统 预警
平均值 1.0 1.2 2.0 2.3 3.4 3.6 1.0 1.6 2.0 2.9 1.0 2.2
最小值 0.1 0.3 1.3 1.5 3.1 3.3 0.1 0.1 1.3 1.3 0.1 0.1
最大值 2.0 2.3 2.8 3.4 4.5 4.7 2.0 3.3 2.8 4.3 2.0 4.1
累计概率15% 0.7 0.9 1.8 2.0 3.3 3.5 0.7 0.9 1.8 2.0 0.7 1.2
累计概率55% 1.0 1.2 2.0 2.3 3.4 3.6 1.0 1.7 2.0 3.2 1.0 2.2
累计概率95% 1.5 1.7 2.5 2.9 3.8 4.0 1.5 2.6 2.5 3.5 1.5 3.4
), ArticleFig(id=1322506480343962173, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506469002564066, language=EN, label=Tab. 2, caption=

Earthquake early warning time of single network and fusion networks

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用时 类型
单网 多网
测震 强震 烈度 测震强震 强震烈度 三网融合
首台 震后 首台 震后 首台 震后 首台 震后 首台 震后 首台 震后
最小值 3.0 5.7 3.0 5.7 3.0 5.7 3.0 5.7 3.0 5.7 3.0 5.7
平均值 5.6 9.9 4.9 8.0 4.1 6.4 4.4 7.5 4.0 6.2 4.0 6.1
最大值 19.0 28.6 14.4 19.4 12.6 16.1 13.0 17.3 8.4 11.7 7.2 11.5
累计概率15% 3.0 7.2 3.0 6.4 3.0 5.9 3.0 6.2 3.0 5.8 3.0 5.8
累计概率55% 4.8 8.9 4.2 7.7 4.0 6.3 4.0 7.3 4.0 6.1 4.0 6.1
累计概率95% 11.0 16.8 8.0 11.9 4.2 7.5 6.4 10.3 4.0 6.8 4.0 6.8
), ArticleFig(id=1322506480415265342, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506469002564066, language=CN, label=表2, caption=

单网和多网融合的地震预警首报时间

, figureFileSmall=null, figureFileBig=null, tableContent=
用时 类型
单网 多网
测震 强震 烈度 测震强震 强震烈度 三网融合
首台 震后 首台 震后 首台 震后 首台 震后 首台 震后 首台 震后
最小值 3.0 5.7 3.0 5.7 3.0 5.7 3.0 5.7 3.0 5.7 3.0 5.7
平均值 5.6 9.9 4.9 8.0 4.1 6.4 4.4 7.5 4.0 6.2 4.0 6.1
最大值 19.0 28.6 14.4 19.4 12.6 16.1 13.0 17.3 8.4 11.7 7.2 11.5
累计概率15% 3.0 7.2 3.0 6.4 3.0 5.9 3.0 6.2 3.0 5.8 3.0 5.8
累计概率55% 4.8 8.9 4.2 7.7 4.0 6.3 4.0 7.3 4.0 6.1 4.0 6.1
累计概率95% 11.0 16.8 8.0 11.9 4.2 7.5 6.4 10.3 4.0 6.8 4.0 6.8
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云南地震台网监测与预警能力评估分析
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李凤英 1 , 许亚吉 1 , 张天继 1 , 吴宇琴 1 , 党文杰 1 , 曹颖 1
大地测量与地球动力学 | 观测技术 2025,45(10): 1085-1092
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大地测量与地球动力学 |观测技术 2025 , 45 (10) : 1085 -1092
云南地震台网监测与预警能力评估分析
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李凤英1 , 许亚吉1, 张天继1, 吴宇琴1, 党文杰1, 曹颖1
作者信息
  • 1 云南省地震局，昆明市北辰大道148号，650224
作者简介:

李凤英，助理工程师，主要从事地震观测技术研究，E-mail：

Evaluation of Monitoring and Warning Capability of Yunnan Province Seismic Network
Fengying LI1 , Yaji XU1, Tianji ZHANG1, Yuqin WU1, Wenjie DANG1, Ying CAO1
Affiliations
  • 1 Yunnan Earthquake Agency, 148 Beichen Road, Kunming 650224, China

    • LI Fengying, assistant engineer, majors in seismic observation, E-mail:

出版时间: 2025-10-15 doi: 10.14075/j.jgg.2024.09.432
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摘要
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利用云南地震台网2 060个站点记录到的72 h连续波形数据，将各台站背景噪声最大概率峰值位移(PGD)作为评估指标，对云南地震台网监测与预警能力进行评估分析。结果显示，云南地震台网平均监测能力达ML1.0，最小可以监测到ML0.1地震，在滇中、滇西北地区监测能力相对薄弱；速度计、加速度计、烈度仪三类传感器融合后的预警最小震级、首台触发和震后预警首报用时平均值分别为ML2.2、4.0 s、6.1 s。云南地震台网总体规划布局满足地震预警初期设置要求。

噪声水平  /  监测能力  /  预警能力  /  地震预警

This study aims to examine the monitoring and early warning abilities of the Yunnan seismic station network using the 72 h continuous waveform recordings recorded at 2 060 stations. The evaluation index used was the maximum probability peak displacement(PGD) of background noise at each station. The findings revealed that the average monitoring capacity of the Yunnan seismic network raised to ML1.0, while the smallest earthquake that could be monitored was ML0.1, and the monitoring capacity was comparatively weak in the central and northwestern Yunnan area. Moreover, the average values of the minimum magnitude of earthquake early warning, the warning time after the triggering of the first station, and the warning time after the earthquake were ML2.2, 4.0 s, and 6.1 s, respectively. Furthermore, the planning layout of the Yunnan seismic station network fulfilled the criteria for the initial establishment of earthquake early warning.

noise level  /  monitoring capability  /  warning capability  /  earthquake early warning
李凤英, 许亚吉, 张天继, 吴宇琴, 党文杰, 曹颖. 云南地震台网监测与预警能力评估分析. 大地测量与地球动力学, 2025 , 45 (10) : 1085 -1092 . DOI: 10.14075/j.jgg.2024.09.432
Fengying LI, Yaji XU, Tianji ZHANG, Yuqin WU, Wenjie DANG, Ying CAO. Evaluation of Monitoring and Warning Capability of Yunnan Province Seismic Network[J]. Journal of Geodesy and Geodynamics, 2025 , 45 (10) : 1085 -1092 . DOI: 10.14075/j.jgg.2024.09.432
正文
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云南省位于印度板块与欧亚板块碰撞汇聚区域，受板块碰撞及俯冲作用影响，该地区构造活动错综复杂、地质活动剧烈，深大断裂广泛发育[1-5]。云南省断裂体系主要可划分为三大类，中部以红河断裂为界展开，西部以澜沧江断裂和怒江断裂为主导，东部则以小江断裂为界限[1-8]。2023—2024年云南省地震震中分布见图 1，该地区地震活动频繁、震级较大、灾害影响深远，是中国中强地震活动频繁且地震灾害最为严重的省份之一[6]，目前关于云南地震台网监测能力的分析尚未见系统性研究成果。本文在充分考虑地震预警时效性的基础上，运用传统地震台网监测能力评估方法，推导出云南地震台网可监测到的最小震级及云南地震预警台网首报用时的空间分布[2-7]。通过全面分析云南地震台网的监测与预警能力，科学评估台网预警效能，旨在为完善地震台网总体布局和地震预报提供科学依据，进而有效提升地震灾害风险防范能力。
1 云南地震台网概况
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云南地震台网由测震台网和预警台网组成，其中测震台网由10个国家级台站和58个省级区域台站构成，均安装有宽频带或甚宽频带地震计。国家烈度速报与预警工程云南子项目台站建设涉及810个一般站、202个基准站(其中新建145个，改建57个)和228个基本站(其中新建119个，改建109个)，云南子项目新建及改建台站总计1 240个。2016年“云南简易烈度计地震预警示范项目”已建成的120个一般站，与云南子项目建设台站实现联网观测，为达成“一乡一台站”的目标，云南自建300个一般站。此外，云南地震台网整合了四川、广西、贵州等邻省332个预警站点数据，台站总数达到2 060个，全网平均台站间距约为10 km，台站分布情况见图 2。在预警基准站中配置了6通道数采、地震计、加速度计，基本站配置了3通道数采和强震动加速度计，一般站配置了烈度仪。为便于计算和讨论，本文将云南地震台网依据仪器类型划分为测震台网、烈度台网、强震台网。本文收集了上述三类台网共2 060个站点在2024-08-08—10记录的72 h背景噪声数据，经过筛选剔除频繁断记的台站后，得到1 440个烈度台、519个强震台和306个测震台的波形记录，以供分析处理。
2 方法
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2.1 台网震级监测能力分析
监测能力是指台网对微小地震识别并能精确定位的能力。通常情况下，监测能力的大小主要依赖于观测台站的环境噪声、观测仪器的动态范围、台网密度等因素。其中，观测台站的环境噪声影响尤为严重，台基噪声越大，对地震波的识别越困难，识别微小地震的能力越弱。
将研究区域以0.5°×0.5°网格化，假定每一个特定的网格中均有地震发生，并将三类传感器噪声记录仿真为周期T=1 s，阻尼比ξ=0.707的DD-1记录噪声波形，则发生在网格内的地震震级可以依据下式计算：
$\begin{aligned}& M_{\mathrm{L}}=\lg U_m+R(\varDelta) \\& U_m=3 \mathrm{PGD}=9 \sigma\end{aligned}$
式中，Um为地震波的最大峰值位移；PGD为最大概率峰值位移；σ为位移噪声的有效值；ML为地方性震级；R(Δ)为云南地震台网量规函数，Δ为震中距。根据GB17740—2017《地震震级的规定》[9]，云南地震台网的量规函数见图 3。
根据震级评估式(1)，对于第m个网格内的第n个台站计算的理论震级为：
$M_{m n}=\lg \left(3 \mathrm{PGD}_n\right)+R(\varDelta m n)$
对于发生在第m个网格以内的地震，各台站测定的理论震级ML由小到大依次为：
$M_{1 m}^{\prime} \leqslant M_{2 m}^{\prime} \leqslant M_{3 m}^{\prime} \leqslant M_{4 m}^{\prime} \leqslant \cdots \leqslant M_{l m}^{\prime}$
依据4台定位触发原则的设定，对于第m个网格的监测能力设定为：
$M_m=M_{4 m}^{\prime}$
将研究区域内的所有网格循环遍历计算，得到云南地震台网监测能力的空间展布变化。
2.2 预警台网最小震级评估
传统地震台网震级监测能力分析不需要考虑时效性，而预警台网最小震级的评估则需要考虑。对于第m个网格中的最小震级则取距离网格最近的前3台或前4台进行定位，假定第m个网格周边前3台或前4台震级为M′1m、M′2m、M′3m、M′4m，根据4台定位原则，第m个网格的最小震级为：
$M_m=\max \left\{M_{1 m}^{\prime}, M_{2 m}^{\prime}, M_{3 m}^{\prime}, M_{4 m}^{\prime}\right\}$
2.3 预警台网首报用时评估
地震预警首报时间的评估是以首台触发时间开始，选取与首台触发时间最近的前3台或前4台，取研究区域的平均震源深度，依据速度模型来计算理论首报时间。假定第m个网格发生地震，预警台网首报用时可表示为：
$t_m=\max \left\{t_{L m}, t_{M m}\right\}$
式中，tLm和tMm为距离第m个网格最近的3台或4台计算震级时间和定位时间。遍历所有网格，即可得到地震预警首报用时的空间分布。
3 计算结果与讨论
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3.1 数据处理
地震台网的监测能力与台基噪声水平有关，台站背景噪声水平直接影响着台网预警能力。本文选取2024-08-08—10云南地震台网记录的72 h连续波形资料，以背景噪声最大峰值位移为标准评估台网噪声水平，剔除异常台站，进而得到整个云南地震台网的监测能力和预警能力的空间分布。具体处理方法如下：以天为单位收集到云南地震台网2 060个台站的连续波形，剔除断记、波形大异常干扰较多的台站，共得到1 440台烈度仪、519台强震动加速度计和306台速度计的SEED波形文件；将SEED文件解压为SAC文件，再对其进行去线性化、去仪器响应等预处理。将三类传感器的噪声记录统一仿真为周期为1 s、阻尼比为0.707的DD-1记录的位移、加速度、速度。将每个台站以60 s为间隔截取记录，计算台站的位移噪声峰值(PGD)。根据概率分布得到每个台站最终的PGD值，将台网噪声统计到的位移噪声峰值(PGD)由小到大排序，累计概率在一定数值之间的为正常值，超过该范围为异常。根据台站PGD值筛选出可用台站，进而得到整个地震台网监测能力和预警能力的空间分布。
3.2 云南地震台网背景噪声分析
云南地震台网速度计、加速度计、烈度仪三类传感器的位移噪声峰值(PGD)统计结果散点分布见图 4，根据筛选上下限剔除异常台站得到各类传感器正常台站PGD值的直方分布图(图 5)和累计概率分布图(图 6)。选取306个测震台PGD值的98%和3%作为上下限，剔除20个异常台站后，得到测震台PGD值的平均值为0.007 μm，最小值为0.003 1 μm，最大值为0.021 μm；519个强震动台上下限分别取为98.5%和1.1%，剔除13个异常台站后，得出强震动台PGD值的平均值为0.17 μm，最小值为0.034 μm，最大值为1.06 μm。同理，1 440个烈度台上下限分别取为99%和0.5%，得出其PGD值的平均值为15.6 μm，最大值为35.4 μm，最小值为8.9 μm。对比三类传感器，速度计的PGD值最小，主要分布在0~0.015 μm之间，其次是加速度计，大多数台站PGD值小于0.5 μm，而烈度计的PGD值最高，主要集中分布在10~25 μm之间。因此，烈度仪、加速度计、速度计三类传感器的噪声水平依次降低。各台站的PGD结果显示，整个云南地震台网噪声分布水平均匀，台网布局合理，地震监测数据可靠。
3.3 云南地震台网监测能力分析
将整个云南地区按0.05°×0.05°划分为201个网格，假定每个网格内均有地震发生，依次计算得到单网和多网融合后的地震监测能力空间分布(图 7)及详细结果(表 1, 单位ML)。
结果显示，测震台网、强震台网、烈度台网在全省范围内的地震监测能力平均值分别为ML1.0、ML2.0、ML3.4。三网融合的理论监测能力平均值为ML1.0，最小可以监测到ML0.1地震，全省95%区域的监测能力可以达到ML1.5，55%区域的监测能力达到ML1.0，局部15%区域可以监测到ML0.7地震，等同于监测能力最强的测震单网。前人基于近震震级公式计算了云南68个测震台的监测能力，发现整个测震台网监测能力平均为ML1.6，局部地区可达ML0.8[10]。造成二者的差异可能是因为台网的密度，台网密度越高，监测能力越强。本文使用了2 060个台站的连续波形数据，远高于原本测震台网数量，因而呈现的监测能力也更强。通过对比，三类台网中，测震台网的监测能力最强，强震台网次之，烈度台网最弱；多网融合后的监测能力等同于监测能力最强的单网。
将云南划分为滇西南、滇西北、滇东南、滇东北、滇中5个区域，通过监测能力空间分布结果可以看出，在滇西北迪庆、怒江及滇中楚雄一带，由于测震台站数量较为稀疏，且分布不均匀，监测能力较弱；而在丽江、大理附近，无论是单网还是多网都显示出较强的监测能力，这与该地区分布有红河断裂、金沙江断裂、澜沧江断裂等属于地震危险区的情况相符。
3.4 地震预警最小震级评估
在考虑地震预警时效性的前提下，依据传统地震台网监测能力的计算方法，对云南地震台网的地震预警最小震级进行评估，得到预警最小震级的空间分布(图 8)和详细结果(表 1, 单位ML)。
可以看出，测震台网地震预警最小震级平均值为ML1.2，最小值为ML0.3，最大值为ML2.3，其中全省95%地区的地震预警最小震级为ML1.7，55%区域的地震预警最小震级为ML1.2，全省15%局部重点区域的地震预警最小震级为ML0.9。强震台网地震预警最小震级平均值为ML2.3，最小值为ML1.5，最大值为ML3.4，其中全省95%地区的地震预警最小震级为ML2.9，55%区域的地震预警最小震级为ML2.3，全省15%局部重点区域的地震预警最小震级为ML2.0。烈度台网地震预警最小震级平均值为ML3.6，最小值为ML3.3，最大值为ML4.7，其中全省95%地区的地震预警最小震级为ML4.0，55%区域的地震预警最小震级为ML3.6，全省15%局部重点区域的地震预警最小震级为ML3.5。
多网融合结果显示，测震强震融合台网测定的地震预警最小震级平均值为ML1.6、最小值为ML0.1、最大值为ML3.3，在全省15%重点区域、55%区域、95%区域的地震预警最小震级分别为ML0.9、ML1.7、ML2.6。强震烈度融合台网测定的地震预警最小震级平均值为ML2.9、最小值为ML1.3、最大值为ML4.3，在全省15%重点区域、55%区域、95%区域的地震预警最小震级分别为ML2.0、ML3.2、ML3.5。三网融合测定的地震预警最小震级平均值为ML2.2、最小值为ML0.1、最大值为ML4.1，在全省15%重点区域、55%区域、95%区域的地震预警最小震级分别为ML1.2、ML2.2、ML3.4。相较于强震烈度融合台网，三网融合后台网地震监测能力平均值降低ML0.7，最小值降低ML1.2。对比单网和多网融合结果，多网融合测定的地震预警最小震级最小值均优于单网测定的最小震级最小值，而其余数值均介于融合台网对应的单网之间。
相较于传统地震监测能力的计算结果，无论是单一网络还是多网络的计算结果，地震预警系统的最小震级阈值均高于传统地震监测能力的计算结果。这种差异的产生，主要源于在评估地震预警系统的时效性时，采用了4台定位原则来确定地震预警的最小震级阈值，该原则仅选取震源网格周边最近的4个台站进行震级测定。相比之下，传统地震监测能力的计算则是基于记录到地震的台站中测定的最大震级来确定该网格的最小监测震级。因此，地震预警系统的最小震级阈值大于传统方法计算得出的地震台网能够监测到的最小震级。
3.5 地震预警首报用时评估
假定震源深度为10 km，依据震后精准定位和测定震级的时间来推算云南地震台网预警首报用时。理论上，在首台触发后，预警系统至少需要3 s才能对地震进行精定位和震级测定，因此地震预警首报用时不低于3 s。通过计算得到云南地震台网地震预警首台触发首报用时和震后首报用时空间分布(图 9和10)及详细结果(表 2, 单位s)。
结果显示，测震台网预警首台触发首报用时平均值为5.6 s，在局部重点区域可以缩短到3 s，全省95%区域可以在首台触发后11 s发出详细信息，55%区域可以在4.8 s发出震后信息。强震台网预警首台触发首报用时平均值为4.9 s，在局部重点区域可以缩短到3 s，全省95%区域可以在8 s发出详细信息，55%区域可以在4.2 s发出震后信息。烈度台网预警首台触发首报用时平均值为4.1 s，在局部重点区域可以缩短到3 s，全省95%区域可以在4.2 s发出详细信息，55%区域可以在4.0 s发出震后信息。测震台网预警震后首报用时平均值为9.9 s，在局部重点区域可以缩短到7.2 s，全省95%区域可以在震后16.8 s发出详细信息，55%区域可以在8.9 s发出震后信息。强震台网预警震后首报用时平均值为8.0 s，在局部重点区域可以缩短到6.4 s，全省95%区域可以在震后11.9 s发出详细信息，55%区域可以在7.7 s发出震后信息。烈度台网预警震后首报用时平均值为6.4 s，在局部重点区域可以缩短到5.9 s，全省95%区域可以在震后7.5 s发出详细信息，55%区域可以在6.3 s发出震后信息。三类传感器中烈度台网相比于测震台网和强震台网台站数量更多、台网密度更加密集均匀，所以无论预警首台触发首报用时还是震后首报用时均优于强震台网和测震台网。
三网融合后的首台触发首报用时和震后首报用时平均值分别为4.0 s、6.1 s，最大用时分别为7.2 s、11.5 s，在全省绝大部分区域具有4.0 s(震后6.8 s)的预警首报用时。相比于烈度台网，三网融合后的首报用时平均值缩短0.1~0.3 s，而最大用时提前4.6~5.4 s，三网融合的结果优于首报用时最短的单网。三网融合后首台触发和震后预警首报用时平均值分别为4.02 s、6.15 s，该结果与前人对云南地区306个地震事件预警首报用时的统计结果相同[3]，进一步说明云南地震台网总体规划布局满足地震预警初期设置要求。
4 结语
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本文利用306台速度计、519台加速度计、1 440台烈度仪记录到的72 h连续波形记录评估云南地震台网的监测与预警能力，得出以下结论：
1) 采用噪声功率谱法得到云南地震台网三类传感器的最大概率峰值位移(PGD)，并以此为指标评估台网噪声水平。结果显示，测震台网的PGD值主要分布在0~0.015 μm之间，平均值为0.007 μm；强震台网的PGD值主要分布在0~0.5 μm之间，平均值为0.17 μm；烈度台网的PGD值主要分布在10~25 μm之间，平均值为15.6 μm。三类台网的噪声水平最高的是烈度台网，其次是强震台网，最低的是测震台网。
2) 依据传统地震监测能力评估方法得出云南地震台网监测能力最低下限，三网融合后的理论监测能力等同于测震单网的结果。综合三类台网的结果得出云南地震台网监测能力平均值为ML1.0，在局部重点区域监测能力值为ML0.7，全省95%区域可以监测到ML1.5地震。考虑地震预警时效性，计算的各类台网地震预警最小震级均大于传统方法得出的结果，且三类台网地震监测能力的空间展布均显示滇中楚雄、怒江、迪庆的监测能力相对薄弱。
3) 依据全国地震预警监测台网密度估计，预警系统需在首台触发后3~5 s(震后10 s)准确计算出地震相关信息，并发布出去[4]。云南地震台网三网融合后首台触发和震后预警首报用时平均值分别为4.0 s、6.1 s，全省95%区域可以在首台触发后4.0 s(震后6.8 s)收到地震信息，局部重点区域可以在首台触发后3.0 s(震后5.8 s)收到相关信息，满足地震预警初期设置的要求。台站数量越多，台网密度越大，预警首报用时越短。
综上所述，科学准确地评估云南地震台网的监测和预警效能，为地震监测预报业务提供有效依据，同时也能为未来地震台网合理布局提供参考，切实有效提高地震台网监测能力。
基金
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  • 云南省地震局青年科技基金(2024k01)
  • 云南省地震局青年科技基金(2023k06)
  • 云南省地震科技创新团队(CXTD202404)
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参考文献 引证文献
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doi: 10.14075/j.jgg.2024.09.432
  • 接收时间：2024-09-19
  • 首发时间：2026-10-29
  • 出版时间：2025-10-15
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  • 收稿日期：2024-09-19
基金
Youth Science Fund of Yunnan Earthquake Agency(2024k01)
云南省地震局青年科技基金(2024k01)
Youth Science Fund of Yunnan Earthquake Agency(2023k06)
云南省地震局青年科技基金(2023k06)
Earthquake Science and Technology Innovation Team of Yunnan Earthquake Agency(CXTD202404)
云南省地震科技创新团队(CXTD202404)
作者信息
    1 云南省地震局，昆明市北辰大道148号，650224
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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