Abstract
Regional seismic networks often observe artificially induced seismic events such as blasting and collapses. Misclassified seismic events in the earthquake catalog can therefore interfere with assessments of natural seismic activity. Traditional methods rely on the period, and phase features of seismic waves to determine the nature of seismic events. We designed three seismic event classifiers with reference to convolutional neural network structures such as VGGnet, ResNet, and Inception. The designed classifiers were tested and compared using three-channel seismic full-waveform time-series data and spectral data. Our classifiers are shown to only require 60 s of full-waveform seismic event data and first-arrival times for alignment; additional phase labeling or numerical filtering is unnecessary. Rapid classification of earthquakes, blasting, and mine collapses can be achieved within approximately 1 min of an event. As a test case, this study uses 6.4 k observations of actual local seismic events with magnitudes ranging from ML 0.6 to ML 4.5 obtained from 47 broadband seismic stations in the Henan Regional Network of the China Seismological Network Center; these observations include natural earthquakes, blasting, and collapse events. The results indicate that our classifiers can reach a lower classification magnitude limit of ML 0.6 and that their recall and accuracy exceed 90%, outperforming manually performed routine classifications and similar approaches. These findings provide an important reference for the rapid classification of small and medium earthquakes.
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Code availability
Name of code: Seismic-classifier.
Developer: Luozhao Jia.
Contact address: Henan Earthquake Agency, No. 10 Zhengguang Road, Zhengzhou City, China.
Email: lezhao.jia@gmail.com.
First year available: 2021.
Required hardware: PC or server.
Required hardware for training environment: NVIDIA RTX2080.
The above graphics card, operating system: Windows or Linux.
Programming language: Python 3.7, Tensorflow 2.0.
The source codes are available for download at the link: https://github.com/epnet2018/Seismic-classifier.
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Acknowledgements
The seismic waveform dataset used in this paper was provided by the Henan Regional Center of the China Seismic Network Center. We thank the anonymous reviewers for their useful comments and suggestions. The seismic waveforms used in this paper can be downloaded from https://data.earthquake.cn. The algorithm was implemented using the Python software package Tensorflow (https://tensorflow.org), tflearn (http://tflearn.org), Matplotlib implementation, keras (https://keras.io), and scikit-learn (http://scikit-learn.org).
Funding
This research was supported by the Earthquake Science Spark Program of China Earthquake Administration no. XH20036.
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Contributions
Luozhao Jia: completed the experiments, program design, and writing.
Hongfeng Chen: contributed to the experimental concept and the analysis of the findings.
Kang Xing: participated in the collection of the data and the production of the illustrations.
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Article highlights
• We designed and compared three deep-learning classifiers to distinguish natural earthquakes, blasting, and collapse using convolutional neural network structures.
• The effects of the time-series input and spectrum on the classifiers were compared.
• The designed classifiers do not need to extract waveform features or mark seismic phases in advance and are suitable for moderate and microseismic events.
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Jia, L., Chen, H. & Xing, K. Rapid classification of local seismic events using machine learning. J Seismol 26, 897–912 (2022). https://doi.org/10.1007/s10950-022-10109-5
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DOI: https://doi.org/10.1007/s10950-022-10109-5