Estimation of Probability Distributions of Geoacoustic Signal Characteristics

Gapeev, Maksim; Senkevich, Yury; Lukovenkova, Olga

doi:10.1088/1742-6596/2096/1/012018

Cited by 1 publication

(1 citation statement)

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“…These findings have great significance for understanding earthquake behavior and the development of earthquake prediction systems. Gapeev et al proposed an empirical model of geoacoustic emission (GAE) signals constructed using statistical methods to help identify abnormal changes in GAE signals, which plays an important role in earthquake prediction [16]. In 2022, Volvach et al [17] discussed the precursor phenomena of statistical changes in physical and biophysical fields related to earthquakes, providing important guidance for reducing losses caused by earthquakes and taking corresponding preventive measures.…”

Section: Related Workmentioning

confidence: 99%

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: Related Workmentioning

confidence: 99%