A Fuzzy Shape-Based Anomaly Detection and its Application to Electromagnetic Data

Vyron Christodoulou, Y Bi, FG Wilkie

Research output: Contribution to journalArticle

Abstract

The problem of data analytics in real world electromagnetic (EM) applications poses a lot of algorithmic constraints. The process of big datasets, the requirement of prior knowledge, unknown location of anomalies and variable length patterns are all issues that need to be addressed. In this application we address those issues by proposing a Fuzzy Symbolic Representational method with anomaly detection (AD). This method is evaluated against twelve benchmark datasets of different kinds of anomalies and provides promising results based on the use of a new performance metric that takes into account the distance between predicted and actual anomalies. Real-world EM data from the Earth’s magnetic field are provided by the SWARM satellite constellation using regions in China, Greece and Peru. The seismic events that occurred in those regions are compared against the SWARM data. Moreover, three other methods: GrammarViz, HOT-SAX and CUSUM-EWMA are also applied to further investigate the possible linkeages of EM anomalies with seismic events. The findings further our understanding of real-world data analytics in EM data and seismicity. Some proposals regarding the limitations of available data for the real-world datasets are also presented.
LanguageEnglish
Pages3366-3379
Number of pages14
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume11
Issue number9
Early online date29 Jul 2018
DOIs
Publication statusPublished - 7 Sep 2018

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anomaly
detection
seismicity
magnetic field
method

Keywords

  • Anomaly Detection
  • Symbolic Representation
  • Fuzzy Logic
  • Electromagnetic Data
  • SWARM Satellites

Cite this

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A Fuzzy Shape-Based Anomaly Detection and its Application to Electromagnetic Data. / Christodoulou, Vyron; Bi, Y; Wilkie, FG.

In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 11, No. 9, 07.09.2018, p. 3366-3379.

Research output: Contribution to journalArticle

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