Scattering wavelet transform for earthquake signals classification with a small training set

References

1. Jenkins, W.F., Gerstoft, P., Bianco, M.J. and Bromirski, P.D. [2021] Unsupervised deep clustering of seismic data: monitoring the Ross ice shelf, Antarctica. Journal of Geophysical Research: Solid Earth, 126(9), e2021JB021716.
   [Google Scholar]
2. Kodera, Y. and Sakai, S. [2018] An unsupervised learning method for the automatic classification of earthquake and noise signals recorded in continuous waveforms. In: AGU Fall Meeting Abstracts, 2018. S13B-02.
   [Google Scholar]
3. Krizhevsky, A., Sutskever, I. and Hinton, G.E. [2012] Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems. 1097–1105.
   [Google Scholar]
4. LeCun, Y., Bengio, Y. and Hinton, G. [2015] Deep learning. nature, 521(7553), 436.
   [Google Scholar]
5. Mallat, S. [2012] Group invariant scattering. Communications on Pure and Applied Mathematics, 65(10), 1331–1398.
   [Google Scholar]
6. Meier, M.A., Ross, Z.E., Ramachandran, A., Balakrishna, A., Nair, S., Kundzicz, P., Li, Z., Andrews, J., Hauksson, E. and Yue, Y. [2019] Reliable Real-Time Seismic Signal/Noise Discrimination With Machine Learning. Journal of Geophysical Research: Solid Earth, 124(1), 788–800.
   [Google Scholar]
7. Mnasri, Z., Rovetta, S. and Masulli, F. [2022] Anomalous sound event detection: A survey of machine learning based methods and applications. Multimedia Tools and Applications, 81(4), 5537–5586.
   [Google Scholar]
8. Mousavi, S.M., Zhu, W., Ellsworth, W. and Beroza, G. [2019] Unsupervised Clustering of Seismic Signals Using Deep Convolutional Autoencoders. IEEE Geoscience and Remote Sensing Letters, 16(11), 1693–1697.
   [Google Scholar]
9. Zhang, H., Zeng, J., Ma, C., Li, T., Deng, Y. and Song, T. [2021] Multi-Classification of Complex Microseismic Waveforms Using Convolutional Neural Network: A Case Study in Tunnel Engineering. Sensors, 21(20), 6762.
   [Google Scholar]
10. Zhang, Z. and Schuller, B. [2012] Semi-supervised learning helps in sound event classification. In: 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 333–336.
    [Google Scholar]