Seismic Event Detection with Fourier Neural Operator

References

1. Allen, R.V. [1978] Automatic earthquake recognition and timing from single traces. Bulletin of the seismological society of America, 68(5), 1521–1532.
   [Google Scholar]
2. Birnie, C. and Hansteen, F [2022] Bidirectional recurrent neural networks for seismic event detection. Geophysics, 87(3), KS97–KS111.
   [Google Scholar]
3. Birnie, C., Jarraya, H. and Hansteen, F [2021] An introduction to distributed training of deep neural networks for segmentation tasks with large seismic data sets. Geophysics, 86(6), KS151–KS160.
   [Google Scholar]
4. Chen, Y. [2020] Automatic microseismic event picking via unsupervised machine learning. Geophysical Journal International, 222(3), 1750–1764.
   [Google Scholar]
5. Gentili, S. and Michelini, A. [2006] Automatic picking of P and S phases using a neural tree. Journal of Seismology, 10, 39–63.
   [Google Scholar]
6. Gibbons, S.J. and Ringdal, F [2006] The detection of low magnitude seismic events using array-based waveform correlation. Geophysical Journal International, 165(1), 149–166.
   [Google Scholar]
7. Kashefi, A. and Mukerji, T. [2024] A novel Fourier neural operator framework for classification of multi-sized images: Application to three dimensional digital porous media. Physics of Fluids, 36(5).
   [Google Scholar]
8. Li, Z., Kovachki, N., Azizzadenesheli, K., Liu, B., Bhattacharya, K., Stuart, A. and Anandkumar, A. [2020] Fourier neural operator for parametric partial differential equations. arXiv preprint arXiv:20I0.08895.
   [Google Scholar]
9. Maity, D., Aminzadeh, F and Karrenbach, M. [2014] Novel hybrid artificial neural network based autopicking workflow for passive seismic data. Geophysical Prospecting, 62(4-Vertical Seismic Profiling and Microseismicity Frontiers), 834–847.
   [Google Scholar]
10. Mousavi, S.M., Sheng, Y., Zhu, W. and Beroza, G.C. [2019a] STanford EArthquake Dataset (STEAD): A Global Data Set of Seismic Signals for AI. IEEE Access.
    [Google Scholar]
11. Mousavi, S.M., Zhu, W., Sheng, Y. and Beroza, G.C. [2019b] CRED: A deep residual network of con-volutional and recurrent units for earthquake signal detection. Scientific reports, 9(1), 10267.
    [Google Scholar]
12. Qu, S., Guan, Z., Verschuur, E. and Chen, Y [2020] Automatic high-resolution microseismic event detection via supervised machine learning. Geophysical Journal International, 222(3), 1881–1895.
    [Google Scholar]
13. Ross, Z.E., Meier, M.A., Hauksson, E. and Heaton, T.H. [2018] Generalized seismic phase detection with deep learning. Bulletin of the Seismological Society of America, 108(5A), 2894–2901.
    [Google Scholar]
14. Shaheen, A., Waheed, U.B., Fehler, M., Sokol, L. and Hanafy, S. [2021] GroningenNet: Deep learning for low-magnitude earthquake detection on a multi-level sensor network. Sensors, 21(23), 8080.
    [Google Scholar]
15. Wilkins, A.H., Strange, A., Duan, Y. and Luo, X. [2020] Identifying microseismic events in a mining scenario using a convolutional neural network. Computers & Geosciences, 137, 104418.
    [Google Scholar]
16. Yoon, C.E., O'Reilly, O., Bergen, K.J. and Beroza, G.C. [2015] Earthquake detection through computationally efficient similarity search. Science advances, 1(11), e1501057.
    [Google Scholar]
17. Zheng, J., Lu, J., Peng, S. and Jiang, T. [2018] An automatic microseismic or acoustic emission arrival identification scheme with deep recurrent neural networks. Geophysical Journal International, 212(2), 1389–1397.
    [Google Scholar]