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An AETA Geoacoustic Signal Anomaly Detection Method Based on FindCBLOF

Published: 28 July 2021 Publication History

Abstract

The multi-component earthquake monitoring and prediction system AETA has deployed more than 200 devices in China and accumulated a large amount of observation data. How to extract anomalies from AETA geoacoustic signal is a problem worthy of attention. This paper proposes an AETA geoacoustic signal anomaly detection method based on findCBLOF. The AETA geoacoustic data is clustered according to the similarity of data in different time periods. The abnormal score is expressed by CBLOF. The abnormal score is compared with the defined threshold to get the final abnormal value. Through experiments on 10 stations in Sichuan, China, the results show that the precision rate of the method proposed in this paper is 70.8%, the recall rate is 61.8%, and the F1-score reaches 65.8%. Compared with other anomaly detection algorithms, the one based on findCBLOF has effective anomaly extraction capability for AETA geoacoustic data, and has a good correspondence with local earthquakes.

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  • (2023)A Clone Selection Algorithm Optimized Support Vector Machine for AETA Geoacoustic Anomaly DetectionElectronics10.3390/electronics1223484712:23(4847)Online publication date: 30-Nov-2023

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cover image ACM Other conferences
ICISS '21: Proceedings of the 4th International Conference on Information Science and Systems
March 2021
166 pages
ISBN:9781450389136
DOI:10.1145/3459955
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 July 2021

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Author Tags

  1. AETA
  2. FindCBLOF
  3. anomaly detection
  4. earthquake monitoring

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  • Shenzhen science and technology

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ICISS 2021

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  • (2023)A Clone Selection Algorithm Optimized Support Vector Machine for AETA Geoacoustic Anomaly DetectionElectronics10.3390/electronics1223484712:23(4847)Online publication date: 30-Nov-2023

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