Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/1874
Authors: Romeo, G. 
Title: Seismic signals detection and classification using artiricial neural networks
Issue Date: Jun-1994
Series/Report no.: 37/3
URI: http://hdl.handle.net/2122/1874
Keywords: seismology
detection
neural network
auto-associative neural network
classification
Subject Classification05. General::05.01. Computational geophysics::05.01.99. General or miscellaneous 
Abstract: 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.
Appears in Collections:Annals of Geophysics

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