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Classification of natural and non-natural earthquake signals based on residual neural networks
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Jie SHEN1, 2, Jingbao ZHU1, 2, Fajun MIAO3, Jindong SONG1, 2, Shanyou LI1, 2
Earthquake Engineering and Engineering Dynamics | 2024, 44(5) : 13 - 25
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Earthquake Engineering and Engineering Dynamics | 2024, 44(5): 13-25
Classification of natural and non-natural earthquake signals based on residual neural networks
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Jie SHEN1, 2, Jingbao ZHU1, 2, Fajun MIAO3, Jindong SONG1, 2, Shanyou LI1, 2
Affiliations
  • 1.Key Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration, Harbin 150080, China
  • 2.Key Laboratory of Earthquake Disaster Mitigation, Ministry of Emergency Management, Harbin 150080, China
  • 3.Earthquake Bureau of Jiangsu Province, Nanjing 210014, China
doi: 10.13197/j.eeed.2024.0502
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Aiming to accurately differentiate between natural and non-natural earthquakes, a neural network model based on one-dimensional convolution and residual structures, named ResNet-1D, was constructed. This model automatically extracts features from three-component seismic records using convolutional layers with convolutional kernels of different lengths, pooling layers composed of max-pooling, and residual structures. The adaptive moment estimation method (Adams) is used to optimize parameters, and a linear discriminant function (Linear) is applied to distinguish between natural and non-natural earthquakes. Using 40000 velocity records of natural and non-natural earthquakes, compiled by the China Earthquake Networks Center from 2008 to 2020, the data was randomly divided into training, validation, and test datasets in a 6∶2∶2 ratio. The test results show that the classification accuracy for natural and non-natural earthquakes is 92.65% and 94.30%, respectively. Compared with traditional machine learning methods, the ResNet-1D model significantly improves the test results in terms of accuracy, precision, recall, and F1 score, effectively enhancing the accuracy of identifying natural and non-natural earthquakes. Moreover, variations in magnitude and epicentral distance also affect the classification accuracy of the model, with higher magnitudes and greater distances resulting in lower accuracy. The model proposed in this paper offers higher accuracy and provides technical support for accurately distinguishing between natural and non-natural earthquakes in seismic monitoring.

residual neural network  /  earthquake signals classification  /  non-natural earthquake  /  natural earthquake  /  earthquake monitoring
Jie SHEN, Jingbao ZHU, Fajun MIAO, Jindong SONG, Shanyou LI. Classification of natural and non-natural earthquake signals based on residual neural networks[J]. Earthquake Engineering and Engineering Dynamics, 2024 , 44 (5) : 13 -25 . DOI: 10.13197/j.eeed.2024.0502
Year 2024 volume 44 Issue 5
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Article Info
doi: 10.13197/j.eeed.2024.0502
  • Receive Date:2024-01-15
  • Online Date:2026-03-30
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  • Received:2024-01-15
  • Revised:2024-02-23
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Affiliations
    1.Key Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration, Harbin 150080, China
    2.Key Laboratory of Earthquake Disaster Mitigation, Ministry of Emergency Management, Harbin 150080, China
    3.Earthquake Bureau of Jiangsu Province, Nanjing 210014, China
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https://castjournals.cast.org.cn/joweb/dzgcygczd/EN/10.13197/j.eeed.2024.0502
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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