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A Fuzzy Inspired Approach to Seismic Anomaly Detection

Christodoulou, Vyron, Bi, Yaxin and Guoze, Zhao (2015) A Fuzzy Inspired Approach to Seismic Anomaly Detection. In: 8th International Conference on Knowledge Science, Engineering and Management, Chongqing, China. Springer. 12 pp. [Conference contribution]

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DOI: 10.1007/978-3-319-25159-2_52

Abstract

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.

Item Type:Conference contribution (Paper)
Keywords:Anomaly Detection, Fuzzy Inference System, Time Series, Fast Fourier Transformation, Electromagnetic Signal, SWARM satellites
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
ID Code:32827
Deposited By: Dr Yaxin Bi
Deposited On:14 Dec 2015 12:05
Last Modified:14 Dec 2015 12:05

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