川滇地区地震诱发滑坡危险性评价模型及软件开发

IF 4 3区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY Geoscientific Model Development Pub Date : 2023-09-06 DOI:10.5194/gmd-16-5113-2023
Xiaoyi Shao, Si-yuan Ma, Chong Xu
{"title":"川滇地区地震诱发滑坡危险性评价模型及软件开发","authors":"Xiaoyi Shao, Si-yuan Ma, Chong Xu","doi":"10.5194/gmd-16-5113-2023","DOIUrl":null,"url":null,"abstract":"Abstract. To enhance the timeliness and accuracy of spatial prediction of\ncoseismic landslides, we propose an improved three-stage spatial prediction\nstrategy and develop corresponding hazard assessment software named\nMat.LShazard V1.0. Based on this software, we evaluate the applicability of\nthis improved spatial prediction strategy in six earthquake events that have\noccurred near the Sichuan–Yunnan region, including the Wenchuan, Ludian,\nLushan, Jiuzhaigou, Minxian, and Yushu earthquakes. The results indicate that\nin the first stage (immediately after the quake event), except for the 2013\nMinxian earthquake, the area under the curve (AUC) values of the modeling performance are above 0.8. Among them, the AUC value of the Wenchuan\nearthquake is the highest, reaching 0.947. The prediction results in the\nfirst stage can meet the requirements of emergency rescue by immediately\nobtaining the overall predicted information of the possible coseismic\nlandslide locations in the quake-affected area. In the second and third\nstages, with the improvement of landslide data quality, the prediction\nability of the model based on the entire landslide database is gradually\nimproved. Based on the entire landslide database, the AUC value of the six\nevents exceeds 0.9, indicating a very high prediction accuracy. For the\nsecond and third stages, the predicted landslide area (Ap) is relatively\nconsistent with the observed landslide area (Ao). However, based on the\nincomplete landslide data in the meizoseismal area, Ap is much smaller than\nAo. When the prediction model based on complete landslide data is built, Ap\nis nearly identical to Ao. This study provides a new application tool for\ncoseismic landslide disaster prevention and mitigation in different stages\nof emergency rescue, temporary resettlement, and late reconstruction after a\nmajor earthquake.\n","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":" ","pages":""},"PeriodicalIF":4.0000,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Hazard assessment modeling and software development of earthquake-triggered landslides in the Sichuan–Yunnan area, China\",\"authors\":\"Xiaoyi Shao, Si-yuan Ma, Chong Xu\",\"doi\":\"10.5194/gmd-16-5113-2023\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract. To enhance the timeliness and accuracy of spatial prediction of\\ncoseismic landslides, we propose an improved three-stage spatial prediction\\nstrategy and develop corresponding hazard assessment software named\\nMat.LShazard V1.0. Based on this software, we evaluate the applicability of\\nthis improved spatial prediction strategy in six earthquake events that have\\noccurred near the Sichuan–Yunnan region, including the Wenchuan, Ludian,\\nLushan, Jiuzhaigou, Minxian, and Yushu earthquakes. The results indicate that\\nin the first stage (immediately after the quake event), except for the 2013\\nMinxian earthquake, the area under the curve (AUC) values of the modeling performance are above 0.8. Among them, the AUC value of the Wenchuan\\nearthquake is the highest, reaching 0.947. The prediction results in the\\nfirst stage can meet the requirements of emergency rescue by immediately\\nobtaining the overall predicted information of the possible coseismic\\nlandslide locations in the quake-affected area. In the second and third\\nstages, with the improvement of landslide data quality, the prediction\\nability of the model based on the entire landslide database is gradually\\nimproved. Based on the entire landslide database, the AUC value of the six\\nevents exceeds 0.9, indicating a very high prediction accuracy. For the\\nsecond and third stages, the predicted landslide area (Ap) is relatively\\nconsistent with the observed landslide area (Ao). However, based on the\\nincomplete landslide data in the meizoseismal area, Ap is much smaller than\\nAo. When the prediction model based on complete landslide data is built, Ap\\nis nearly identical to Ao. This study provides a new application tool for\\ncoseismic landslide disaster prevention and mitigation in different stages\\nof emergency rescue, temporary resettlement, and late reconstruction after a\\nmajor earthquake.\\n\",\"PeriodicalId\":12799,\"journal\":{\"name\":\"Geoscientific Model Development\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2023-09-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Geoscientific Model Development\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.5194/gmd-16-5113-2023\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GEOSCIENCES, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geoscientific Model Development","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.5194/gmd-16-5113-2023","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 1

摘要

摘要为了提高地震滑坡空间预测的及时性和准确性,我们提出了一种改进的三阶段空间预测策略,并开发了相应的灾害评估软件dMat。LShazard V1.0。基于该软件,我们评估了这种改进的空间预测策略在四川-云南地区附近发生的6次地震事件中的适用性,包括汶川、鲁甸、庐山、九寨沟、岷县和玉树地震。结果表明,在第一阶段(地震事件发生后不久),除2013年岷县地震外,建模性能的曲线下面积(AUC)值均在0.8以上。其中,文川地震的AUC值最高,达到0.947。第一阶段的预测结果可以立即获得地震灾区可能的同震滑坡位置的总体预测信息,从而满足应急救援的要求。在第二和第三阶段,随着滑坡数据质量的提高,基于整个滑坡数据库的模型的可预测性逐渐提高。基于整个滑坡数据库,六个事件的AUC值超过0.9,表明预测精度非常高。对于第二和第三阶段,预测的滑坡面积(Ap)与观测到的滑坡区域(Ao)相对一致。然而,根据强震区完整的滑坡资料,Ap远小于Ao。当建立基于完整滑坡数据的预测模型时,Apis与Ao几乎相同。本研究为大地震后不同阶段的应急救援、临时安置和后期重建提供了一种新的应用工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Hazard assessment modeling and software development of earthquake-triggered landslides in the Sichuan–Yunnan area, China
Abstract. To enhance the timeliness and accuracy of spatial prediction of coseismic landslides, we propose an improved three-stage spatial prediction strategy and develop corresponding hazard assessment software named Mat.LShazard V1.0. Based on this software, we evaluate the applicability of this improved spatial prediction strategy in six earthquake events that have occurred near the Sichuan–Yunnan region, including the Wenchuan, Ludian, Lushan, Jiuzhaigou, Minxian, and Yushu earthquakes. The results indicate that in the first stage (immediately after the quake event), except for the 2013 Minxian earthquake, the area under the curve (AUC) values of the modeling performance are above 0.8. Among them, the AUC value of the Wenchuan earthquake is the highest, reaching 0.947. The prediction results in the first stage can meet the requirements of emergency rescue by immediately obtaining the overall predicted information of the possible coseismic landslide locations in the quake-affected area. In the second and third stages, with the improvement of landslide data quality, the prediction ability of the model based on the entire landslide database is gradually improved. Based on the entire landslide database, the AUC value of the six events exceeds 0.9, indicating a very high prediction accuracy. For the second and third stages, the predicted landslide area (Ap) is relatively consistent with the observed landslide area (Ao). However, based on the incomplete landslide data in the meizoseismal area, Ap is much smaller than Ao. When the prediction model based on complete landslide data is built, Ap is nearly identical to Ao. This study provides a new application tool for coseismic landslide disaster prevention and mitigation in different stages of emergency rescue, temporary resettlement, and late reconstruction after a major earthquake.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Geoscientific Model Development
Geoscientific Model Development GEOSCIENCES, MULTIDISCIPLINARY-
CiteScore
8.60
自引率
9.80%
发文量
352
审稿时长
6-12 weeks
期刊介绍: Geoscientific Model Development (GMD) is an international scientific journal dedicated to the publication and public discussion of the description, development, and evaluation of numerical models of the Earth system and its components. The following manuscript types can be considered for peer-reviewed publication: * geoscientific model descriptions, from statistical models to box models to GCMs; * development and technical papers, describing developments such as new parameterizations or technical aspects of running models such as the reproducibility of results; * new methods for assessment of models, including work on developing new metrics for assessing model performance and novel ways of comparing model results with observational data; * papers describing new standard experiments for assessing model performance or novel ways of comparing model results with observational data; * model experiment descriptions, including experimental details and project protocols; * full evaluations of previously published models.
期刊最新文献
Enabling high-performance cloud computing for the Community Multiscale Air Quality Model (CMAQ) version 5.3.3: performance evaluation and benefits for the user community. Impacts of updated reaction kinetics on the global GEOS-Chem simulation of atmospheric chemistry. Understanding changes in cloud simulations from E3SM version 1 to version 2 Development of inter-grid-cell lateral unsaturated and saturated flow model in the E3SM Land Model (v2.0) WRF (v4.0)–SUEWS (v2018c) coupled system: development, evaluation and application
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1