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Authors: | Romeo, G. | Title: | Seismic signals detection and classification using artiricial neural networks | Issue Date: | Jun-1994 | Series/Report no.: | 3/37 (1994) | URI: | http://hdl.handle.net/2122/1874 | Keywords: | seismology detection neural network auto-associative neural network classification |
Subject Classification: | 05. 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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