Spatio-temporal network modelling and analysis of global strong earthquakes (Mw ≥ 6.0)

被引：1

作者：

Liu, Gang ^{[1
,2
]}

Fan, Qinjin ^{[3
]}

Li, Weile ^{[1
]}

Scaringi, Gianvito ^{[4
]}

Long, Yujie ^{[2
]}

He, Jing ^{[2
]}

Li, Zheng ^{[5
]}

机构：

[1] Chengdu Univ Technol, State Key Lab Geohazard Prevent & Geoenvironm Pro, Chengdu 610059, Peoples R China

[2] Chengdu Univ Technol, Coll Earth Sci, Chengdu 610059, Peoples R China

[3] Univ Georgia, Dept Geog, Athens, GA 30602 USA

[4] Charles Univ Prague, Fac Sci, Inst Hydrogeol Engn Geol & Appl Geophys, Prague 12843, Czech Republic

[5] Sichuan Prov Land & Space Planning Res Inst, Chengdu 610084, Peoples R China

来源：

JOURNAL OF THE GEOLOGICAL SOCIETY | 2020年 / 177卷 / 05期

基金：

中国国家自然科学基金;

关键词：

LARGE AFTERSHOCKS; SCALING LAW; SEISMICITY;

D O I：

10.1144/jgs2019-151

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

We employ a spatio-temporal network modelling approach to identify possible relations between strong earthquakes and spatial regions worldwide. A global strong earthquake dataset containing 7736 events (M-w >= 6.0) from 1964 to 2018 is used. Statistical results identify power-law relationships and heavy tail phenomena in the spatial patterns of strong earthquakes. The interactions between regions follow the same law, with a few regions that may be hit by successive strong earthquakes with high probability. Also, we find that the interconnections between regions are mainly related to the succession of events in time, whereas the distribution of events is extremely inhomogeneous in space. This study provides a research prototype for the spatiotemporal analysis of global strong earthquakes, laying a foundation for obtaining insights into the network modelling approach for global strong earthquakes.

引用

页码：883 / 892

页数：10

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