Classification of earthquakes, explosions and mining-induced earthquakes based on XGBoost algorithm

The classification of low-magnitude tectonic earthquakes, explosions and mining-induced earthquakes is an important task in regional earthquake monitoring. Seismic events occurring at local and regional distances are classified primarily based on the characteristics of their waveform. We established...

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Published in:	Computers & geosciences Vol. 170; p. 105242
Main Authors:	Wang, Tingting, Bian, Yinju, Zhang, Yixiao, Hou, Xiaolin
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.01.2023
Subjects:	data collection earthquakes Feature extraction geophysics Model performance index Seismic events identification spectral analysis support vector machines tectonics XGBoost algorithm Feature extraction XGBoost algorithm Seismic events identification Model performance index
ISSN:	0098-3004, 1873-7803
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Abstract	The classification of low-magnitude tectonic earthquakes, explosions and mining-induced earthquakes is an important task in regional earthquake monitoring. Seismic events occurring at local and regional distances are classified primarily based on the characteristics of their waveform. We established 36-dimensional and 201-dimensional datasets by using feature extraction and amplitude spectral analysis. The Extreme Gradient Boosting (XGBoost) supervised algorithm is introduced for the discrimination of couples-class and three-class. The accuracies in the earthquakes/explosions discrimination with feature extraction dataset and amplitude spectrum dataset are 97.48% and 95.12%, respectively, which shows that feature extraction can effectively quantify the differences between earthquakes and explosions. For the classification of earthquakes/mining-induced earthquakes and explosions/mining-induced earthquakes, the performance of XGBoost with the amplitude spectrum dataset is greater, with accuracies of 99.24% and 95.33%, respectively. In the classification of the three types of events, the accuracies of XGBoost are 96.41% for earthquakes, 90.38% for explosions, and 94.04% for mining-induced earthquakes. The performance indices of XGBoost for different input parameters are invariably greater than those of the support vector machine (SVM), with stable classification ability, suggesting that the XGBoost model has good prospects for application in seismic event classification. •Apply XGBoost Algorithm to study the classification of seismic events occurring at local and regional distances.•The input dataset is based on feature extraction and amplitude spectrum.•Compare the performance of XGBoost and SVM using confusion matrix.•XGBoost outperforms SVM in different performance indices (AUC, Accuracy, TPR, TNR).
AbstractList	The classification of low-magnitude tectonic earthquakes, explosions and mining-induced earthquakes is an important task in regional earthquake monitoring. Seismic events occurring at local and regional distances are classified primarily based on the characteristics of their waveform. We established 36-dimensional and 201-dimensional datasets by using feature extraction and amplitude spectral analysis. The Extreme Gradient Boosting (XGBoost) supervised algorithm is introduced for the discrimination of couples-class and three-class. The accuracies in the earthquakes/explosions discrimination with feature extraction dataset and amplitude spectrum dataset are 97.48% and 95.12%, respectively, which shows that feature extraction can effectively quantify the differences between earthquakes and explosions. For the classification of earthquakes/mining-induced earthquakes and explosions/mining-induced earthquakes, the performance of XGBoost with the amplitude spectrum dataset is greater, with accuracies of 99.24% and 95.33%, respectively. In the classification of the three types of events, the accuracies of XGBoost are 96.41% for earthquakes, 90.38% for explosions, and 94.04% for mining-induced earthquakes. The performance indices of XGBoost for different input parameters are invariably greater than those of the support vector machine (SVM), with stable classification ability, suggesting that the XGBoost model has good prospects for application in seismic event classification. •Apply XGBoost Algorithm to study the classification of seismic events occurring at local and regional distances.•The input dataset is based on feature extraction and amplitude spectrum.•Compare the performance of XGBoost and SVM using confusion matrix.•XGBoost outperforms SVM in different performance indices (AUC, Accuracy, TPR, TNR). The classification of low-magnitude tectonic earthquakes, explosions and mining-induced earthquakes is an important task in regional earthquake monitoring. Seismic events occurring at local and regional distances are classified primarily based on the characteristics of their waveform. We established 36-dimensional and 201-dimensional datasets by using feature extraction and amplitude spectral analysis. The Extreme Gradient Boosting (XGBoost) supervised algorithm is introduced for the discrimination of couples-class and three-class. The accuracies in the earthquakes/explosions discrimination with feature extraction dataset and amplitude spectrum dataset are 97.48% and 95.12%, respectively, which shows that feature extraction can effectively quantify the differences between earthquakes and explosions. For the classification of earthquakes/mining-induced earthquakes and explosions/mining-induced earthquakes, the performance of XGBoost with the amplitude spectrum dataset is greater, with accuracies of 99.24% and 95.33%, respectively. In the classification of the three types of events, the accuracies of XGBoost are 96.41% for earthquakes, 90.38% for explosions, and 94.04% for mining-induced earthquakes. The performance indices of XGBoost for different input parameters are invariably greater than those of the support vector machine (SVM), with stable classification ability, suggesting that the XGBoost model has good prospects for application in seismic event classification.
ArticleNumber	105242
Author	Wang, Tingting Bian, Yinju Zhang, Yixiao Hou, Xiaolin
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Snippet	The classification of low-magnitude tectonic earthquakes, explosions and mining-induced earthquakes is an important task in regional earthquake monitoring....
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SubjectTerms	data collection earthquakes Feature extraction geophysics Model performance index Seismic events identification spectral analysis support vector machines tectonics XGBoost algorithm
Title	Classification of earthquakes, explosions and mining-induced earthquakes based on XGBoost algorithm
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