Forecast of seismic aftershocks using a Neural Network

被引：0

作者：

Lin, FC ^{[1
]}

Elhassan, N ^{[1
]}

Hassan, A ^{[1
]}

Yousif, A ^{[1
]}

机构：

[1] Univ Maryland Eastern Shore, Dept Math & Comp Sci, Princess Anne, MD 21853 USA

来源：

ICONIP'02: PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON NEURAL INFORMATION PROCESSING: COMPUTATIONAL INTELLIGENCE FOR THE E-AGE | 2002年

关键词：

aftershock; earthquake; forecast; Neural Network; Levenberg-Marquardt algorithm;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Every significant earthquake is followed by a mostly identifiable cluster of aftershocks. To predict the occurrence of these aftershocks, we trained a Neural Network using seismic data from SCSN(Caltech) as input. The trained network is extrapolated recursively, using the last target as the next input. In this way we were able to reproduce the three major aftershocks with magnitude 4.0 or greater for the main shock of magnitude 5.2 on Jan. 7, 1996 in Southern California. This paradigm returns a deterministic result, but requires two adjustable parameters: the number of hidden nodes and the tolerance.

引用

页码：1796 / 1799

页数：4