An Easy-To-Update Pulse-Like Ground Motion Identification Method Based on Siamese Convolutional Neural Networks
JOURNAL OF EARTHQUAKE ENGINEERING(2024)
摘要
A pulse-like ground motion identification method is proposed based on the Siamese Convolutional Neural Networks (SCNNs). The wavelet coefficient graphs of pulse-like ground motions are used as the input data, and the features extracted by the SCNNs are used for the identification. Based on the time-domain features, pulse-like and non-pulse-like ground motions are classified into several classes. The results indicated that all the identified pulse-like ground motions have similar pulse features to the pre-selected training data. The principal advantage of the method is that the misclassification problem can be minimized by an updating procedure proposed by this paper.
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关键词
Pulse-like ground motion identification,Siamese Convolutional Neural Networks,wavelet analysis,near-fault,image classification
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