Rapid Learning of Earthquake Felt Area and Intensity Distribution with Real-time Search Engine Queries

doi:10.1038/s41598-020-62114-8

	Rapid Learning of Earthquake Felt Area and Intensity Distribution with Real-time Search Engine Queries
	Zhu, Hengshu 1; Sun, Ying 1,2,3; Zhao, Wenjia 4; Zhuang, Fuzhen 2,3; Wang, Baoshan 5; Xiong, Hui 6
	2020-03-25
发表期刊	SCIENTIFIC REPORTS
ISSN	2045-2322
卷号	10 期号:1 页码:9
摘要	Immediately after a destructive earthquake, the real-time seismological community has a major focus on rapidly estimating the felt area and the extent of ground shaking. This estimate provides critical guidance for government emergency response teams to conduct orderly rescue and recovery operations in the damaged areas. While considerable efforts have been made in this direction, it still remains a realistic challenge for gathering macro-seismic data in a timely, accurate and cost-effective manner. To this end, we introduce a new direction to improve the information acquisition through monitoring the real-time information-seeking behaviors in the search engine queries, which are submitted by tens of millions of users after earthquakes. Specifically, we provide a very efficient, robust and machine-learning-assisted method for mapping the user-reported ground shaking distribution through the large-scale analysis of real-time search queries from a dominant search engine in China. In our approach, each query is regarded as a "crowd sensor" with a certain weight of confidence to proactively report the shaking location and extent. By fitting the epicenters of earthquakes occurred in mainland China from 2014 to 2018 with well-designed machine learning models, we can efficiently learn the realistic weight of confidence for each search query and sketch the felt areas and intensity distributions for most of the earthquakes. Indeed, this approach paves the way for using real-time search engine queries to efficiently map earthquake felt area in the regions with a relatively large population of search engine users.
DOI	10.1038/s41598-020-62114-8
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[91746301] ; National Natural Science Foundation of China[61773361] ; National Key Research and Development Program of China[2018YFB1004300]
WOS研究方向	Science & Technology - Other Topics
WOS类目	Multidisciplinary Sciences
WOS记录号	WOS:000560014500019
出版者	NATURE PUBLISHING GROUP
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/15801
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhu, Hengshu; Xiong, Hui
作者单位	1.Baidu Inc, Beijing 100085, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, CAS, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.China Earthquake Adm, Inst Geol, Beijing 100029, Peoples R China 5.Univ Sci & Technol China, Sch Earth & Space Sci, Hefei 230026, Peoples R China 6.Rutgers State Univ, Newark, NJ 07102 USA
推荐引用方式 GB/T 7714	Zhu, Hengshu,Sun, Ying,Zhao, Wenjia,et al. Rapid Learning of Earthquake Felt Area and Intensity Distribution with Real-time Search Engine Queries[J]. SCIENTIFIC REPORTS,2020,10(1):9.
APA	Zhu, Hengshu,Sun, Ying,Zhao, Wenjia,Zhuang, Fuzhen,Wang, Baoshan,&Xiong, Hui.(2020).Rapid Learning of Earthquake Felt Area and Intensity Distribution with Real-time Search Engine Queries.SCIENTIFIC REPORTS,10(1),9.
MLA	Zhu, Hengshu,et al."Rapid Learning of Earthquake Felt Area and Intensity Distribution with Real-time Search Engine Queries".SCIENTIFIC REPORTS 10.1(2020):9.