Archives of Acoustics, 48, 3, pp. 403–412, 2023

VMD and CNN-Based Classification Model for Infrasound Signal

Quanbo LU
China University of Geosciences

Mei LI
China University of Geosciences

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.
Keywords: infrasound signal; variational mode decomposition; convolutional neural network; fast Fourier transform
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Copyright © 2024 The Author(s). This work is licensed under the Creative Commons Attribution 4.0 International CC BY 4.0.


Albert S., Linville L. (2020), Benchmarking current and emerging approaches to infrasound signal classification, Seismological Research Letters, 91(2A): 921–929, doi: 10.1785/0220190116.

Asming V.E., Fedorov A.V., Fedorov I.S., Asming S.V. (2022), Algorithms for the detection, location, and discrimination of seismic and infrasound events, Izvestiya, Atmospheric and Oceanic Physics, 58(11): 1398–1417, doi: 10.1134/S0001433822110019.

Cofferb B.E., Parker M.D. (2022), Infrasound signals in simulated nontornadic and pre-tornadic supercells, The Journal of the Acoustical Society of America, 151(2): 939–954, doi: 10.1121/10.0009400.

De Angelis S., Diaz-Moreno A., Zuccarello L. (2019), Recent developments and applications of acoustic infrasound to monitor volcanic emissions, Remote Sensing, 11(11): 1302, doi: 10.3390/rs11111302.

Dragomiretskiy K., Zosso D. (2014), Variational mode decomposition, IEEE Transactions on Signal Processing, 62(3): 531–544, doi: 10.1109/TSP.2013.2288675.

Eckel F., Langer H., Sciotto M. (2023), Monitoring sources of volcanic activity at Mount Etna using pattern recognition techniques on infrasound signals, Geophysical Journal International, 232(1): 1–16, doi: 10.1093/gji/ggac278.

Garcia R.F. et al. (2021), Search for infrasound signals in InSight data using coupled pressure/ground deformation methods, Bulletin of the Seismological Society of America, 111(6): 3055–3064, doi: 10.1785/0120210079.

Garcia R.F. et al. (2022), Infrasound from large earthquakes recorded on a network of balloons in the stratosphere, Geophysical Research Letters, 49(15): e2022GL098844, doi: 10.1029/2022GL098844.

Gi N., Brown P. (2017), Refinement of bolide characteristics from infrasound measurements, Planetary and Space Science, 143: 169–181, doi: 10.1016/j.pss.2017.04.021.

Ham F.M., Rekab K., Acharyya R., Lee Y.-C. (2008), Infrasound signal classification using parallel RBF Neural Networks, International Journal of Signal and Imaging Systems Engineering, 1(3–4): 155–167, doi: 10.1504/IJSISE.2008.026787.

Hupe P., Ceranna L., Le Pichon A., Matoza R.S., Mialle P. (2022), International Monitoring System infrasound data products for atmospheric studies and civilian applications, Earth System Science Data, 14(9): 4201–4230, doi: 10.5194/essd-14-4201-2022.

Iezzi A.M., Schwaiger H.F., Fee D., Haney M.M. (2019), Application of an updated atmospheric model to explore volcano infrasound propagation and detection in Alaska, Journal of Volcanology and Geothermal Research, 371: 192–205, doi: 10.1016/j.jvolgeores.2018.03.009.

Lawrence S., Giles C.L., Tsoi A.C., Back A.D. (1997), Face recognition: A convolutional neural-network approach, IEEE Transactions on Neural Networks, 8(1): 98–113, doi: 10.1109/72.554195.

Leng X.-P., Liu D.-L., Wei F.-Q., Hong Y., Dai D.-F. (2017), Debris flows monitoring and localization using infrasonic signals, Journal of Mountain Science, 14(7): 1279–1291, doi: 10.1007/s11629-016-3836-3.

Li M., Liu X.Y., Liu X. (2016), Infrasound signal classification based on spectral entropy and support vector machine, Applied Acoustics, 113: 116–120, doi: 10.1016/j.apacoust.2016.06.019.

Liu D., Tang D., Zhang S., Leng X., Hu K., He L. (2021), Method for feature analysis and intelligent recognition of infrasound signals of soil landslides, Bulletin of Engineering Geology and the Environment, 80(2): 917–932, doi: 10.1007/s10064-020-01982-w.

Liu X.Y., Li M., Tang W.,Wang S.C., Wu X. (2014), A new classification method of infrasound events using Hilbert-Huang transform and support vector machine, Mathematical Problems in Engineering, 2014(3): 1–6, doi: 10.1155/2014/456818.

Mayer S., Van Herwijnen A., Ulivieri G., Schweizer J. (2020), Evaluating the performance of an operational infrasound avalanche detection system at three locations in the Swiss Alps during two winter seasons, Cold Regions Science and Technology, 173: 102962, doi: 10.1016/j.coldregions.2019.102962.

Perttu A., Taisne B., De Angelis S., Assink J.D., Tailpied D., Williams R.A. (2020), Estimates of plume height from infrasound for regional volcano monitoring, Journal of Volcanology and Geothermal Research, 402: 106997, doi: 10.1016/j.jvolgeores.2020.106997.

Thüring T., Schoch M., Van Herwijnena A., Schweizer J. (2015), Robust snow avalanche detection using supervised machine learning with infrasonic sensor arrays, Cold Regions Science and Technology, 111: 60–66, doi: 10.1016/j.coldregions.2014.12.014.

Van Der Maaten L., Hinton G. (2008), Visualizing data using t-SNE, Journal of Machine Learning Research, 9(11): 2579–2605.

Watson L.M. et al. (2022), Volcano infrasound: progress and future directions, Bulletin of Volcanology, 84(5): 44, doi: 10.1007/s00445-022-01544-w.

Witsil A., Fee D., Dickey J., Peña R.,Waxler R., Blom P. (2022), Detecting large explosions with machine learning models trained on synthetic infrasound data, Geophysical Research Letters, 49(11): e2022GL097785, doi: 10.1029/2022GL097785.

Witsil A.J., Johnson J.B. (2020), Analyzing continuous infrasound from Stromboli volcano, Italy using unsupervised machine learning, Computers & Geosciences, 140: 104494, doi: 10.1016/j.cageo.2020.104494.

Yang X., Su W., Jiang C., Bian Y. (2022), Research progress on infrasound generation mechanism, monitoring technology and application, Progress in Geophysics, 37(1): 78–93, doi: 10.6038/pg2022FF0081.

Zhang M., Gao L., Zhang X., Zhang S. (2022), An infrasound source localisation algorithm for improving location accuracy of gas pipeline leakage detection system, International Journal of Embedded Systems, 15(1): 9–18, doi: 10.1504/IJES.2022.122042.

Zhang S. et al. (2020), Model test: Infrasonic features of porous soil masses as applied to landslide monitoring. Engineering Geology, 265: 105454, doi: 10.1016/j.enggeo.2019.105454.

Zhu X., Xu Q., Liu H.X. (2017), Using Hilbert–Huang transform (HHT) to extract infrasound generated by the 2013 Lushan earthquake in China, Pure and Applied Geophysics, 174(3): 865–874, doi: 10.1007/s00024-016-1438-1.

DOI: 10.24425/aoa.2023.145247