Identifying Asperity Patterns Via Machine Learning Algorithms

被引：4
|
作者：
Arvanitakis, Kostantinos [1 ]
Avlonitis, Markos [1 ]
机构：
[1] Ionian Univ, Dept Informat, Corfu 49100, Greece
来源：
ARTIFICIAL INTELLIGENCE APPLICATIONS AND INNOVATIONS, AIAI 2016 | 2016年 / 475卷
关键词：
Asperity; Density; b-value; Seismicity; Machine learning; RUPTURE PROCESS; EARTHQUAKE;
D O I：
10.1007/978-3-319-44944-9_8
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
An asperity's location is very crucial in the spatiotemporal analysis of an area's seismicity. In literature, b-value and seismic density have been proven as useful indicators for asperity location. In this paper, machine learning techniques are used to locate areas with high probability of asperity existence using as feature vector information extracted solely by earthquake catalogs. Many machine learning algorithms are tested to identify those with the best results. This method is tested for data from the wider region of Hokkaido, Japan where in an earlier study asperities have been detected.
引用
收藏
页码：87 / 93
页数：7
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