INGe: Intensity-ground motion dataset for Italy

I. Oliveti, L. Faenza, A. Michelini
{"title":"INGe: Intensity-ground motion dataset for Italy","authors":"I. Oliveti, L. Faenza, A. Michelini","doi":"10.5194/essd-2020-372","DOIUrl":null,"url":null,"abstract":"Abstract. In this paper we present an updated and homogeneous earthquake data set for Italy compiled by joining the Italian Macroseismic Database DBMI15 and the Engineering Strong-Motion (ESM) accelerometric data bank. The database has been compiled through an extensive procedure of selection and revision based on two main steps: 1) the removal of several earthquakes in DBMI15 because the data source has been considered to be largely unreliable and 2) the extraction of all the localities reporting intensity data which are located within 3 km from the accelerograph stations that recorded the data. The final data set includes 323 recordings from 65 earthquakes and 227 stations in the time span 1972–2016. The events are characterized by magnitudes in the range 4.0–6.9 and depths in the range 0.3–45.0 km. Here, we illustrate the data collection and the properties of the database in terms of recording, event and station distributions as well as Mercalli-Cancani-Sieberg (MCS) macroseismic intensity points. Furthermore, we discuss the most relevant features of engineering interest showing several statistics with reference to the most significant metadata (such as moment magnitude, several distance metrics, style of faulting etc). The data set can be downloaded from data repository Zenodo at https://doi.org/10.13127/inge.1 (Oliveti et., 2020).","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"os-48 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Earth System Science Data Discussions","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5194/essd-2020-372","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Abstract. In this paper we present an updated and homogeneous earthquake data set for Italy compiled by joining the Italian Macroseismic Database DBMI15 and the Engineering Strong-Motion (ESM) accelerometric data bank. The database has been compiled through an extensive procedure of selection and revision based on two main steps: 1) the removal of several earthquakes in DBMI15 because the data source has been considered to be largely unreliable and 2) the extraction of all the localities reporting intensity data which are located within 3 km from the accelerograph stations that recorded the data. The final data set includes 323 recordings from 65 earthquakes and 227 stations in the time span 1972–2016. The events are characterized by magnitudes in the range 4.0–6.9 and depths in the range 0.3–45.0 km. Here, we illustrate the data collection and the properties of the database in terms of recording, event and station distributions as well as Mercalli-Cancani-Sieberg (MCS) macroseismic intensity points. Furthermore, we discuss the most relevant features of engineering interest showing several statistics with reference to the most significant metadata (such as moment magnitude, several distance metrics, style of faulting etc). The data set can be downloaded from data repository Zenodo at https://doi.org/10.13127/inge.1 (Oliveti et., 2020).
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
英格:意大利的大地运动强度数据集
摘要本文结合意大利大地震数据库DBMI15和工程强震(ESM)加速度数据库编制了意大利最新的均匀地震数据集。数据库的编制经过了广泛的选择和修订过程,主要有两个步骤:1)删除DBMI15中的几次地震,因为数据源被认为在很大程度上是不可靠的;2)提取距离记录数据的加速度台站3公里范围内的所有报告强度数据的地点。最终的数据集包括1972年至2016年期间来自65次地震和227个站点的323条记录。这些地震的特征是震级在4.0-6.9级之间,深度在0.3-45.0公里之间。在这里,我们从记录、事件和台站分布以及MCS (mercalli - can卡尼- sieberg)大地震烈度点的角度说明数据收集和数据库的特性。此外,我们还讨论了与工程兴趣最相关的特征,显示了参考最重要的元数据(如矩量、几种距离度量、断层类型等)的几种统计数据。该数据集可从数据存储库Zenodo下载,网址为https://doi.org/10.13127/inge.1 (Oliveti et., 2020)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
New SMOS SSS maps in the framework of the Earth Observation data For Science and Innovation in the Black Sea LGHAP: a Long-term Gap-free High-resolution Air Pollutants concentration dataset derived via tensor flow based multimodal data fusion Pre- and post-production processes along supply chains increasingly dominate GHG emissions from agri-food systems globally and in most countries Last Interglacial sea-level data points from Northwest Europe A machine learning approach to address air quality changes during the COVID-19 lockdown in Buenos Aires, Argentina
×
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