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Article

A Clone Selection Algorithm Optimized Support Vector Machine for AETA Geoacoustic Anomaly Detection

by
Qiyi He
1,
Han Wang
1,
Changyi Li
1,
Wen Zhou
1,*,
Zhiwei Ye
1,
Liang Hong
2,
Xinguo Yu
2,
Shengjie Yu
2 and
Lu Peng
3
1
School of Computer Science, Hubei University of Technology, Wuhan 430068, China
2
Hubei Provincial Geographical National Conditions Monitoring Center, Wuhan 430070, China
3
School of Computer Science, Wuhan University, Wuhan 430070, China
*
Author to whom correspondence should be addressed.
Electronics 2023, 12(23), 4847; https://doi.org/10.3390/electronics12234847
Submission received: 15 October 2023 / Revised: 20 November 2023 / Accepted: 26 November 2023 / Published: 30 November 2023
(This article belongs to the Special Issue AI in Disaster, Crisis, and Emergency Management)
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Abstract

Anomaly in geoacoustic emission is an important earthquake precursor. Current geoacoustic anomaly detection methods are limited by their low signal-to-noise ratio, low intensity, sample imbalance, and low accuracy. Therefore, this paper proposes a clone selection algorithm optimized one-class support vector machine method (CSA-OCSVM) for geoacoustic anomaly detection. First, the interquartile range (IQR), cubic spline interpolation, and time window are designed to amplify the geoacoustic signal intensity and energy change rules to reduce the interference of geoacoustic signal noise and intensity. Secondly, to address the imbalance of positive and negative samples in geoacoustic anomaly detection, a one-class support vector machine is introduced for anomaly detection. Meanwhile, in view of the optimization capabilities of the clone selection algorithm, it is adopted to optimize the hyperparameters of OCSVM to improve its detection accuracy. Finally, the proposed model is applied to geoacoustic data anomaly detection in nine different datasets, which are derived from our self-developed acoustic electromagnetic to AI (AETA) system, to verify its effectiveness. By designing comparative experiments with IQR, genetic algorithm OCSVM (GA-OCSVM), particle swarm optimization OCSVM (PSO-OCSVM), and evaluating the performance of the true positive rate (TPR) and false positive rate (FPR), the experimental results depict that the proposed model is superior to the existing state-of-the-art geoacoustic anomaly detection approaches.
Keywords: acoustic electromagnetic to AI; clonal selection algorithm; geoacoustic anomaly detection; one-class support vector machine acoustic electromagnetic to AI; clonal selection algorithm; geoacoustic anomaly detection; one-class support vector machine

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MDPI and ACS Style

He, Q.; Wang, H.; Li, C.; Zhou, W.; Ye, Z.; Hong, L.; Yu, X.; Yu, S.; Peng, L. A Clone Selection Algorithm Optimized Support Vector Machine for AETA Geoacoustic Anomaly Detection. Electronics 2023, 12, 4847. https://doi.org/10.3390/electronics12234847

AMA Style

He Q, Wang H, Li C, Zhou W, Ye Z, Hong L, Yu X, Yu S, Peng L. A Clone Selection Algorithm Optimized Support Vector Machine for AETA Geoacoustic Anomaly Detection. Electronics. 2023; 12(23):4847. https://doi.org/10.3390/electronics12234847

Chicago/Turabian Style

He, Qiyi, Han Wang, Changyi Li, Wen Zhou, Zhiwei Ye, Liang Hong, Xinguo Yu, Shengjie Yu, and Lu Peng. 2023. "A Clone Selection Algorithm Optimized Support Vector Machine for AETA Geoacoustic Anomaly Detection" Electronics 12, no. 23: 4847. https://doi.org/10.3390/electronics12234847

APA Style

He, Q., Wang, H., Li, C., Zhou, W., Ye, Z., Hong, L., Yu, X., Yu, S., & Peng, L. (2023). A Clone Selection Algorithm Optimized Support Vector Machine for AETA Geoacoustic Anomaly Detection. Electronics, 12(23), 4847. https://doi.org/10.3390/electronics12234847

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