A Multi-feature Anomaly Detection Method Based on AETA ULF Electromagnetic Disturbance Signal

There have been many studies in relationship between ultra-low frequency electromagnetic anomaly and earthquakes, while most of them judge anomaly using single feature. We propose a multi-feature anomaly detection method for AETA ULF electromagnetic disturbance signals based on Isolation Forest, wit...

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Bibliographic Details
Published in2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) Vol. 1; pp. 1103 - 1108
Main Authors Liu, Cong, Yong, Shanshan, Wang, Xin'an, Zhang, Xing
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2020
Subjects
AETA
Conferences
Correlation
Earthquakes
Feature extraction
Forestry
Isolation Forest
multi-feature anomaly detection
Process control
SEA
ULF electromagnetic disturbance
Uncertainty
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Summary:There have been many studies in relationship between ultra-low frequency electromagnetic anomaly and earthquakes, while most of them judge anomaly using single feature. We propose a multi-feature anomaly detection method for AETA ULF electromagnetic disturbance signals based on Isolation Forest, with some feature extraction and selection method added. A statistical test method superposed epoch analysis (SEA) is used for its evaluation. The result shows that 6 of 12 selected stations show significant correlation between signal anomaly and earthquakes. A further comparison experiment shows that our method has better performance than traditional single-feature sliding IQR method, which indicates multi-feature might be a good choice in finding global anomaly points.
DOI:10.1109/ITNEC48623.2020.9085032