Open AccessSeismic signals detection and classification using artiricial neural networks
Author(s) -
G. Romeo
Publication year - 1994
Publication title -
annals of geophysics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.394
H-Index - 60
eISSN - 2037-416X
pISSN - 1593-5213
DOI - 10.4401/ag-4211
Subject(s) - artificial neural network , computer science , perceptron , task (project management) , operator (biology) , associative property , class (philosophy) , artificial intelligence , pattern recognition (psychology) , signal (programming language) , time delay neural network , event (particle physics) , mathematics , biochemistry , chemistry , management , repressor , transcription factor , pure mathematics , economics , gene , programming language , physics , quantum mechanics
Pattern recognition belongs to a class of Problems which are easily solved by humans, but difficult for computers. It is sometimes difficult to formalize a problem which a human operator can casily understand by using examples. Neural networks are useful in solving this kind of problem. A neural network may, under certain conditions, simulate a well trained human operator in recognizing different types of earthquakes or in detecting the presence of a seismic event. It is then shown how a fully connected multi layer perceptron may perform a recognition task. It is shown how a self training auto associative neural network may detect an earthquake occurrence analysing the change in signal characteristics