Article(id=1236319263200440873, tenantId=1146029695717560320, journalId=1235980550691926019, issueId=1236316193171239730, articleNumber=null, orderNo=null, doi=10.3969/j.issn.0253-6099.2023.03.008, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1672588800000, receivedDateStr=2023-01-02, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1772691082047, onlineDateStr=2026-03-05, pubDate=1685548800000, pubDateStr=2023-06-01, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1772691082047, onlineIssueDateStr=2026-03-05, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1772691082047, creator=13701087609, updateTime=1772691082047, updator=13701087609, issue=Issue{id=1236316193171239730, tenantId=1146029695717560320, journalId=1235980550691926019, year='2023', volume='43', issue='3', pageStart='1', pageEnd='179', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1772690350096, creator=13701087609, updateTime=1772691872061, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1236322576809652696, tenantId=1146029695717560320, journalId=1235980550691926019, issueId=1236316193171239730, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1236322576809652697, tenantId=1146029695717560320, journalId=1235980550691926019, issueId=1236316193171239730, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=33, endPage=37, ext={EN=ArticleExt(id=1236319264102216270, articleId=1236319263200440873, tenantId=1146029695717560320, journalId=1235980550691926019, language=EN, title=Early Warning and Prediction Approach Based on Ground-Based SAR Data, columnId=1236276106018484431, journalTitle=Mining and Metallurgical Engineering, columnName=MINING, runingTitle=null, highlight=null, articleAbstract=

In view of the data of ground-based SAR presenting high oscillation and high fluctuation with less obvious characteristic trend, an approach for early warning and prediction of slope deformation by using velocity reciprocity was proposed, which adopts a data processing method of subtracting dislocations and deleting limits to optimize the quality of deformation data, thus the accuracy of early warning and timely prediction of landslide can be improved. The application of this approach in an iron mine in Inner Mongolia shows that not only those problems faced in the actual actuation can be solved, but also the data about disaster approaching can present obvious deformation rule. It is concluded that this early warming and prediction approach has a wide application.

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针对地基合成孔径雷达数据振荡性高、波动性强、特征趋势不明显问题,提出了一种应用速度倒数指标对边坡变形进行预警预报的方法,采用错位相减、去限值数据处理方法,优化形变数据的质量,提高滑坡预警准确性、预报及时性。该方法在内蒙古某铁矿的应用结果表明,它能解决监测数据振荡性高、特征趋势弱、预报时效性差等问题,凸显临灾数据形变规律,是一种适用性很强的预警预报方法。

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杨凤芸(1965—),女,辽宁鞍山人,博士,教授,硕士研究生导师,主要研究方向为边坡动力灾害监测、误差理论数据处理及分析。
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刘玉溪(1997—),男,山东日照人,硕士研究生,主要研究方向为边坡动力灾害监测及预警。

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刘玉溪(1997—),男,山东日照人,硕士研究生,主要研究方向为边坡动力灾害监测及预警。

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刘玉溪(1997—),男,山东日照人,硕士研究生,主要研究方向为边坡动力灾害监测及预警。

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基于地基合成孔径雷达数据的预警预报方法研究
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刘玉溪 1 , 杨凤芸 1 , 秦宏楠 2
矿冶工程杂志 | 采矿 2023,43(3): 33-37
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矿冶工程杂志 | 采矿 2023, 43(3): 33-37
基于地基合成孔径雷达数据的预警预报方法研究
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刘玉溪1, 杨凤芸1, 秦宏楠2
作者信息
  • 1.辽宁科技大学 土木工程学院,辽宁 鞍山 114051
  • 2.中国安全生产科学研究院,北京 100012
  • 刘玉溪(1997—),男,山东日照人,硕士研究生,主要研究方向为边坡动力灾害监测及预警。

通讯作者:

杨凤芸(1965—),女,辽宁鞍山人,博士,教授,硕士研究生导师,主要研究方向为边坡动力灾害监测、误差理论数据处理及分析。
Early Warning and Prediction Approach Based on Ground-Based SAR Data
Yuxi LIU1, Fengyun YANG1, Hongnan QIN2
Affiliations
  • 1.School of Civil Engineering, Liaoning University of Science and Technology, Anshan 114051, Liaoning, China
  • 2.China Academy of Safety Science & Technology, Beijing 100012, China
出版时间: 2023-06-01 doi: 10.3969/j.issn.0253-6099.2023.03.008
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针对地基合成孔径雷达数据振荡性高、波动性强、特征趋势不明显问题,提出了一种应用速度倒数指标对边坡变形进行预警预报的方法,采用错位相减、去限值数据处理方法,优化形变数据的质量,提高滑坡预警准确性、预报及时性。该方法在内蒙古某铁矿的应用结果表明,它能解决监测数据振荡性高、特征趋势弱、预报时效性差等问题,凸显临灾数据形变规律,是一种适用性很强的预警预报方法。

矿山地质灾害  /  地质灾害监测  /  滑坡  /  地基合成孔径雷达  /  预警  /  预报  /  矿山安全  /  滑坡

In view of the data of ground-based SAR presenting high oscillation and high fluctuation with less obvious characteristic trend, an approach for early warning and prediction of slope deformation by using velocity reciprocity was proposed, which adopts a data processing method of subtracting dislocations and deleting limits to optimize the quality of deformation data, thus the accuracy of early warning and timely prediction of landslide can be improved. The application of this approach in an iron mine in Inner Mongolia shows that not only those problems faced in the actual actuation can be solved, but also the data about disaster approaching can present obvious deformation rule. It is concluded that this early warming and prediction approach has a wide application.

mine geological disasters  /  geological hazard monitoring  /  landslide  /  ground-based SAR  /  early warning  /  prediction  /  mine safety  /  landslide
刘玉溪, 杨凤芸, 秦宏楠. 基于地基合成孔径雷达数据的预警预报方法研究. 矿冶工程杂志, 2023 , 43 (3) : 33 -37 . DOI: 10.3969/j.issn.0253-6099.2023.03.008
Yuxi LIU, Fengyun YANG, Hongnan QIN. Early Warning and Prediction Approach Based on Ground-Based SAR Data[J]. Mining and Metallurgical Engineering, 2023 , 43 (3) : 33 -37 . DOI: 10.3969/j.issn.0253-6099.2023.03.008
  • 国家重点研发计划(2021YFC3001900)
2023年第43卷第3期
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doi: 10.3969/j.issn.0253-6099.2023.03.008
  • 接收时间:2023-01-02
  • 首发时间:2026-03-05
  • 出版时间:2023-06-01
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  • 收稿日期:2023-01-02
基金
国家重点研发计划(2021YFC3001900)
作者信息
    1.辽宁科技大学 土木工程学院,辽宁 鞍山 114051
    2.中国安全生产科学研究院,北京 100012

通讯作者:

杨凤芸(1965—),女,辽宁鞍山人,博士,教授,硕士研究生导师,主要研究方向为边坡动力灾害监测、误差理论数据处理及分析。
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2种不同金属材料的力学参数

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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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