2020
DOI: 10.1109/access.2020.2982366
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Investigate Contribution of Multi-Microseismic Data to Rockburst Risk Prediction Using Support Vector Machine With Genetic Algorithm

Abstract: As a severe hazard in coal mining and rock excavation, the rockburst is usually induced by the high energy tremor. Microseismic (MS) monitoring is suggested to forecast the rockburst risk to reduce its damage. The paper aims to investigate contribution of multi-MS data, including MS raw wave data and MS energy data, to prediction of the high energy tremor, using support vector machine (SVM) together with genetic algorithm (GA). MS monitoring data recorded for more than 400 days at Wudong coal mine of Xinjiang,… Show more

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Cited by 28 publications
(7 citation statements)
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“…The model was modified by combining the mean impact value algorithm (MIVA), the modified firefly algorithm (MFA), and PNN (MIVA-MFA-PNN model). Ji et al [41] developed a genetic algorithm (GA) and SVM based model (GA-SVM) to analyze microseismic data to predict rockburst occurrence. Table 1 depicts the traditional supervised machine learning approaches proposed by the researchers for predicting rockburst.…”
Section: Introductionmentioning
confidence: 99%
“…The model was modified by combining the mean impact value algorithm (MIVA), the modified firefly algorithm (MFA), and PNN (MIVA-MFA-PNN model). Ji et al [41] developed a genetic algorithm (GA) and SVM based model (GA-SVM) to analyze microseismic data to predict rockburst occurrence. Table 1 depicts the traditional supervised machine learning approaches proposed by the researchers for predicting rockburst.…”
Section: Introductionmentioning
confidence: 99%
“…e classical models include Kuz-Ram model and Rosin-Rammer model [3][4][5]. Because blasting is a complex nonlinear process, these classical theoretical models are based on certain assumptions and consider few influencing factors, so they all have certain limitations [6][7][8][9].…”
Section: Introductionmentioning
confidence: 99%
“…Bing Ji et al classified high energy tremors from general MS events and optimized parameters using support vector machine (SVM) and the genetic algorithm respectively. The results showed that the classifier achieves 98% sensitivity, 88% accuracy and 87% specificity using both MS raw wave and energy data, which is better than solely utilizing MS raw wave or energy data [7]. Pingan Peng et al proposed an automatic classification method based on a deep learning approach for MS records classifying in underground mines.…”
Section: Introductionmentioning
confidence: 99%