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Seismic Signal Classification using Multi-Layer Perceptron Neural Network

by El Hassan Ait Laasri, Es-said Akhouayri, Driss Agliz, Abderrahman Atmani
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 79 - Number 15
Year of Publication: 2013
Authors: El Hassan Ait Laasri, Es-said Akhouayri, Driss Agliz, Abderrahman Atmani
10.5120/13821-1950

El Hassan Ait Laasri, Es-said Akhouayri, Driss Agliz, Abderrahman Atmani . Seismic Signal Classification using Multi-Layer Perceptron Neural Network. International Journal of Computer Applications. 79, 15 ( October 2013), 35-43. DOI=10.5120/13821-1950

@article{ 10.5120/13821-1950,
author = { El Hassan Ait Laasri, Es-said Akhouayri, Driss Agliz, Abderrahman Atmani },
title = { Seismic Signal Classification using Multi-Layer Perceptron Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { October 2013 },
volume = { 79 },
number = { 15 },
month = { October },
year = { 2013 },
issn = { 0975-8887 },
pages = { 35-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume79/number15/13821-1950/ },
doi = { 10.5120/13821-1950 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:53:07.244121+05:30
%A El Hassan Ait Laasri
%A Es-said Akhouayri
%A Driss Agliz
%A Abderrahman Atmani
%T Seismic Signal Classification using Multi-Layer Perceptron Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 79
%N 15
%P 35-43
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The aim of the present study is to investigate and explore the capability of the multilayer perceptron neural network to classify seismic signals recorded by the local seismic network of Agadir (Morocco). The problem is divided into two main steps, the feature extraction step and classification step. In the former, relevant discriminant features are extracted from the seismic signal based on the time and frequency domains. These are selected based on the analysts' experience. In the latter step, a process of trial an error was carried out to find the best neural network architecture. Classification results on a data set of 343 seismic signals have demonstrated that the accuracy of the proposed classier can achieve more than 94%.

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Index Terms

Computer Science
Information Sciences

Keywords

Seismic signal classification multilayer perceptron neural network feature extraction.

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