Forecast of seismic aftershocks using a neural network

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 in...

Full description

Saved in:
Bibliographic Details
Main Authors: Lin, F.C., Elhassan, N., Hassan, A., Yousif, A.
Format: Conference Proceeding
Language:English
Subjects:
Clustering algorithms
Computer science
Data analysis
Earthquakes
Electric shock
Equations
Geology
Neural networks
Stress
Thumb
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 tolerance.
DOI:10.1109/ICONIP.2002.1198983