A fuzzy expert system for automatic seismic signal classification

El Hassan Ait Laasri, Es-Saïd Akhouayri, Dris Agliz, Daniele Zonta, Abderrahman Atmani

Research output: Contribution to journalArticle

23 Citations (Scopus)

Abstract

Automatic classification of seismic events is of great importance due to the large amount of data received continuously. Seismic analysts classify events by visual inspection and calculation of event signal characteristics. This process is subjective and demands hard work as well as a significant amount of time and considerable experience. A reliable automatic classification task considerably reduces the effort required and makes classification faster and more objective. The aim of this study is to develop a fuzzy rule based expert classification system that is able to imitate human reasoning and incorporate the analyst's knowledge of seismic event classification. The fundamental idea behind using this approach was motivated by the way in which human analysts classify seismic events based on a set of experiential rules. Additionally, this approach was chosen due to its interpretability and adjustability, as well as its ability to manage the complexity of real data. Relevant discriminant features are extracted from event signal. Using these features, the classification system was built based on the vote by multiple rule fuzzy reasoning method with three types of rules. Comparison of this method with the single winner classical fuzzy reasoning model was carried out. Classification results on real seismic data showed the robustness of the classifier and its capability to operate in on-line classification.

Original languageEnglish
Pages (from-to)1013-1027
Number of pages15
JournalExpert Systems with Applications
Volume42
Issue number3
Early online date27 Aug 2014
DOIs
Publication statusPublished - 15 Feb 2015

Keywords

  • fuzzy reasoning
  • fuzzy rule based expert system
  • fuzzy set
  • seismic signal classification
  • seismic signal feature extraction

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