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Abstract

Infrasound signal classification is vital in geological hazard monitoring systems. The traditional classification approach extracts the features and classifies the infrasound events. However, due to the manual feature extraction, its classification performance is not satisfactory. To deal with this problem, this paper presents a classification model based on variational mode decomposition (VMD) and convolutional neural network (CNN). Firstly, the infrasound signal is processed by VMD to eliminate the noise. Then fast Fourier transform (FFT) is applied to convert the reconstructed signal into a frequency domain image. Finally, a CNN model is established to automatically extract the features and classify the infrasound signals. The experimental results show that the classification accuracy of the proposed classification model is higher than the other model by nearly 5%. Therefore, the proposed approach has excellent robustness under noisy environments and huge potential in geophysical monitoring.
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Authors and Affiliations

Quanbo Lu
1
ORCID: ORCID
Mei Li
1

  1. School of Information Engineering, China University of Geosciences, Beijing, China
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Abstract

The subject of the article are lit erary and non-literary allusions in the poem by G. Derzhaw in "To Tsarevich Khlor" entitled. They referred to the political and social events that took place during his lifetime. Some o f the allusions he explained in the Commentaries he wrote to his poems, others explain ed in an ambiguous way.

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Authors and Affiliations

Anna Warda

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