A Fuzzy Inspired Approach to Seismic Anomaly Detection

Vyron Christodoulou, Yaxin Bi, Zhao Guoze

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)


In this work we investigate the use of a fuzzy inspired ap- proach for anomaly detection in different electromagnetic time series datasets. The method proposed uses simple methods in a serialized way to achieve anomaly detection. Each method is a smaller component of the system. Each of them adds an element towards the anomaly detection: A smoothing filter removes any unwanted noise, an automated peak finding with Fast Fourier Transformation and correlation reduces the dimension- ality of the signal, a fuzzy inference system encodes the signal before the final comparison and respective output. The proposed method is evalu- ated in 5 benchmark datasets with promising results and the F-Score is used to demonstrate its performance. The method is also evaluated in real datasets gathered from the SWARM satellites for the detection of possible anomalies prior and post to a seismic event. The preliminary experimental results prove to be promising for the proposed method for the detection of anomalies in electromagnetic time series datasets.
Original languageEnglish
Title of host publicationUnknown Host Publication
Number of pages12
Publication statusPublished (in print/issue) - Oct 2015
Event8th International Conference on Knowledge Science, Engineering and Management - Chongqing, China
Duration: 1 Oct 2015 → …


Conference8th International Conference on Knowledge Science, Engineering and Management
Period1/10/15 → …


  • Anomaly Detection
  • Fuzzy Inference System
  • Time Series
  • Fast Fourier Transformation
  • Electromagnetic Signal
  • SWARM satellites


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