Recognition of earthquake-prone areas: Methodology and analysis of the results

被引:42
|
作者
Soloviev, A. A. [1 ]
Gvishiani, A. D. [2 ]
Gorshkov, A. I. [1 ]
Dobrovolsky, M. N. [2 ]
Novikova, O. V. [1 ]
机构
[1] Russian Acad Sci, Inst Earthquake Predict Theory & Math Geophys, Moscow 117997, Russia
[2] Russian Acad Sci, Geophys Ctr, Moscow 119296, Russia
基金
俄罗斯基础研究基金会;
关键词
PATTERN-RECOGNITION; SEISMOGENIC NODES; HIGH SEISMICITY; IDENTIFICATION; CRITERIA; ALPS; EPICENTERS; DINARIDES; CAUCASUS; PYRENEES;
D O I
10.1134/S1069351314020116
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
We present the results of verifying the areas that were detected as prone to strong earthquakes by the pattern recognition algorithms in different regions of the world with different levels of seismicity and, therefore, different threshold magnitudes demarcating the strong earthquakes. The analysis is based on the data presented in the catalog of the U.S. National Earthquake Information Center (NEIC) as of August 1, 2012. In each of the regions considered, we examined the locations of the epicenters of the strong earthquakes that occurred in the region after the publication of the corresponding result. There were 91 such earthquakes in total. The epicenters of 79 of these events (87%) fall in the recognized earthquake-prone areas, including 27 epicenters located in the areas where no strong earthquakes had ever been documented up to the time of publication of the result. Our analysis suggests that the results of the recognition of areas prone to strong earthquakes are reliable and that it is reasonable to use these results in the applications associated with the assessment of seismic risks. The comparison of the recognition for California with the analysis of seismicity of this region by the Discrete Perfect Sets (DPS) algorithm demonstrates the agreement between the results obtained by these two different methods.
引用
收藏
页码:151 / 168
页数:18
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