Publications

[1] Matthaiou, I., Masoudi, A., Araki, E., Kodaira, S., Modafferi, S. and Brambilla, G. (2023) Classifying space-time images obtained from distributed acoustic sensing. Optica Sensing Congress, Munich, Germany. 31 Jul - 04 Aug 2023.

[2] Matthaiou, I., Masoudi, A. and Brambilla, G. (2023) Processing strain data generated from distributed acoustic sensing for monitoring tasks. 28th International Conference on Optical Fibre Sensors, Hamamatsu, Japan. 20 - 24 Nov 2023. 

[3] (submitted) Matthaiou, I., Masoudi, A., Araki, E., Kodaira, S., Modafferi, S. and Brambilla, G. (2023) On the classification of images derived from submarine fibre optic sensing: detecting broadband seismic activity from hydroacoustic signals. Geophysical Journal International.

Resources

[1] Lindsey, N. J., & Eileen R. M., 2021. Fiber-optic seismology, Annual Review of Earth and Planetary Sciences,
49, 309–336.

[2] Karrenbach, M., Ellwood, R., Yartsev, V., Cole, S., Araki, E., Kimura, T. & Matsumoto, H., 2021. Turning
the Muroto seafloor cable into a long DAS sensing array, In Proceedings of the 14th SEGJ International
Symposium, Tokyo, Japan, 18–21 October 2021.

[3] Biondi, B.L., Yuan, S., Martin, E.R., Huot, F. & Clapp, R.G., 2021. Using telecommunication fiber infrastructure
for earthquake monitoring and near-surface characterization, Distributed Acoustic Sensing in
Geophysics: Methods and Applications, John Wiley & Sons, 268, 131–148.

[4] Harmon, N., Rychert, C.A., Davis, J., Brambilla, G., Buffet,W., Chichester, B., Dai, Y., Bogiatzis, P., Snook,
J., van Putten, L. & Masoudi, A., 2022. Surface deployment of DAS systems: Coupling strategies and
comparisons to geophone data. Near Surface Geophysics, 20, 465–477.

[5] Hernandez, P.D., Ram´ırez, J.A. & Soto, M.A., 2021. Deep-learning-based earthquake detection for fiberoptic
distributed acoustic sensing, Journal of Lightwave Technology, 40, 2639–2650.

[6] Masoudi, Ali, Pilgrim, James A., Newson, Trevor P. and Brambilla, Gilberto (2019) Subsea cable condition monitoring with distributed optical fiber vibration sensor. Journal of Lightwave Technology, 37 (4), 1352-1358, [8611347].

[7] Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I. & Salakhutdinov, R.R., 2012. Improving neural
networks by preventing co-adaptation of feature detectors, Preprint, arXiv:1207.0580.

[8] Huot, F., Ariel L., Paige G., Bin L., Robert G. C., Tamas N., Kurt T. N., & Biondo L. B., 2022. Detection and
characterization of microseismic events from fiber-optic DAS data using deep learning, Seismological
Society of America, 93, 2543–2553.

[9] Matsumoto, H., Araki, E., Kimura, T., Fujie, G., Shiraishi, K., Tonegawa, T., Obana, K., Arai, R., Kaiho,
Y., Nakamura, Y. & Yokobiki, T., 2021. Detection of hydroacoustic signals on a fiber-optic submarine
cable, Scientific reports, 11, 2797.

[10] Mousavi, S.M., & Beroza, G.C., 2023. Machine Learning in Earthquake Seismology, Annual Review of
Earth and Planetary Sciences, 51.

[11] Mousavi, S.M., & Beroza, G.C., 2022. Deep-learning seismology, Science, 377.
Mousavi, S.M., Zhu, W., Ellsworth, W. & Beroza, G., 2019. Unsupervised clustering of seismic signals
using deep convolutional autoencoders, IEEE Geoscience and Remote Sensing Letters, 16, 1693–1697.

[12] Mousavi, S.M., Horton, S.P., Langston, C.A. & Samei, B., 2016. Seismic features and automatic discrimination
of deep and shallow induced-microearthquakes using neural network and logistic regression,
Geophys. J Int., , 207, 29–46.

[13] Shahriari, B., Swersky, K., Wang, Z., Adams, R.P. & De Freitas, N., 2015. Taking the human out of the
loop: A review of Bayesian optimization, Proceedings of the IEEE, 104, 148–175.

[14] Snoek, J., Larochelle, H. & Adams, R.P., 2012. Practical Bayesian Optimization of Machine Learning Algorithms,
Advances in Neural Information Processing Systems, 25.

[15] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. & Salakhutdinov, R., 2014. Dropout: a simple way
to prevent neural networks from overfitting, The Journal of Machine Learning Research, 15, 1929–1958.

[16] Szeliski, R., 2022. Computer vision: algorithms and applications, Springer Nature.