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Seismic Signal Classification using Multi-Layer Perceptron Neural Network

International Journal of Computer Applications
© 2013 by IJCA Journal
Volume 79 - Number 15
Year of Publication: 2013
El Hassan Ait Laasri
Es-saïd Akhouayri
Driss Agliz
Abderrahman Atmani

El Hassan Ait Laasri, Es-said Akhouayri, Driss Agliz and Abderrahman Atmani. Article: Seismic Signal Classification using Multi-Layer Perceptron Neural Network. International Journal of Computer Applications 79(15):35-43, October 2013. Full text available. BibTeX

	author = {El Hassan Ait Laasri and Es-said Akhouayri and Driss Agliz and Abderrahman Atmani},
	title = {Article: Seismic Signal Classification using Multi-Layer Perceptron Neural Network},
	journal = {International Journal of Computer Applications},
	year = {2013},
	volume = {79},
	number = {15},
	pages = {35-43},
	month = {October},
	note = {Full text available}


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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