An AETA Geo-sound Anomaly Detection Method Based on Baer Operator

Lyu, Yaxuan; Wang, Xinan; Yong, Shanshan; Huang, Jiayao

doi:10.1145/3325730.3325768

Cited by 1 publication

(3 citation statements)

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“…In the same way, FPR refers to the ratio of negative samples misjudged as positive by the algorithm model to the total number of actual negative samples, which can be described as shown in Equation (9). FPR = FP/FP + TN (9) This study will use the above-mentioned two evaluation indicators, TPR and FPR, to evaluate the effect of the CSA-OCSVM algorithm model on geoacoustic signal anomaly detection.…”

Section: Detection and Assessmentmentioning

confidence: 99%

“…The geoacoustic signal anomaly detection in our study is a binary classification task; therefore, we introduce the IQR [9], GA-OCSVM, and PSO-OCSVM [13] for comparison with our proposed CSA-OCSVM. The advantage of IQR is that it is not affected by the 25% values at both ends.…”

Section: The Prototype Implementationmentioning

confidence: 99%

“…The significant changes in the frequency and intensity of geoacoustic data indicate a shift in the crustal tension state, suggesting a possible precursor to an earthquake. Lyu et al [9] implemented the sliding IQR to compute the abnormal feature of the Baer index sequence of geoacoustic data in AETA; however, the accuracy was not satisfactory. In [10], to capture abnormal geoacoustic signals, a pattern recognition algorithm was designed, and the original geoacoustic data were converted into abnormal feature values.…”

Section: Introductionmentioning

confidence: 99%

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

He,

Wang,

et al. 2023

Electronics

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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.

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Section: Detection and Assessmentmentioning

confidence: 99%

Section: The Prototype Implementationmentioning

confidence: 99%