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Seismic signals detection and classification using artiricial neural networks

By G. Romeo

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

Topics: seismology, detection, neural network, auto-associative neural network, classification, Geophysics. Cosmic physics, QC801-809, Physics, QC1-999, Science, Q, DOAJ:Geophysics and Geomagnetism, DOAJ:Earth and Environmental Sciences, Meteorology. Climatology, QC851-999
Publisher: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
Year: 1994
DOI identifier: 10.4401/ag-4211
OAI identifier: oai:doaj.org/article:ad643be9ab834f9ab442a13cfba89217
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