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• W4313397514 startingPage "107723" @default.
• W4313397514 abstract "Detection of dynamic triggering of earthquakes combined with microseismic monitoring allows a better understanding on rock damage process in rock engineering, which involve local stress disturbances caused by far-field earthquakes. However, rapid and efficient detection of dynamic triggering of earthquakes remain clueless due to the lack of enough investigation. This study proposed a novel method for automatic detection of dynamic triggering of earthquakes based on convolutional neural networks (CNNs). Results show that the trained model is capable of detecting earthquakes with a high accuracy rate of 98.3%, which provided strong supports for the automatic detection of dynamic triggering of earthquakes. This method is applied to the Xiaojiang fault region, and dynamically triggered earthquakes were spotted for 31 of the 47 selected earthquakes. Our method achieves 94% recognition accuracy on the detection of dynamic triggering of earthquakes. This method offers a rapid and accurate framework to detect dynamic triggering of earthquakes and investigate the damage to rock engineering structures caused by far-field earthquakes based on microseismic monitoring." @default.
• W4313397514 created "2023-01-06" @default.
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• W4313397514 date "2023-02-01" @default.
• W4313397514 modified "2023-10-16" @default.
• W4313397514 title "AI-powered automatic detection of dynamic triggering of earthquake based on microseismic monitoring" @default.
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• W4313397514 doi "https://doi.org/10.1016/j.soildyn.2022.107723" @default.
• W4313397514 hasPublicationYear "2023" @default.
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