VMD and CNN-Based Classification Model for Infrasound Signal

• Autorzy: Lu Quanbo , Li Mei

Identyfikatory

DOI

10.24425/aoa.2023.145247

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

infrasound signal; variational mode decomposition; convolutional neural network; Fast Fourier Transform

• Wydawca: Instytut Podstawowych Problemów Techniki PAN ; Komitet Akustyki PAN ; Polskie Towarzystwo Akustyczne
• Czasopismo: Archives of Acoustics
• Rocznik: 2023
• Tom: Vol. 48, No. 3
• Strony: 403--412
• Opis fizyczny: Bibliogr. 28 poz., map., rys., tab., wykr.

Twórcy

autor

Lu Quanbo

• School of Information Engineering, China University of Geosciences Beijing, China

autor

Li Mei

• School of Information Engineering, China University of Geosciences Beijing, China

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023). (PL)