GNGTS 2023 - Atti del 41° Convegno Nazionale

Session 1.1 GNGTS 2023 Hulbert, C., Rouet-Leduc, B., Johnson, P. A., Ren, C. X., Riviere, J., Bolton, D. C., & Marone, C. (2018). Laboratory earthquake prediction illu- minates connections between the spectrum of fault slip modes. Nature Geoscience , 12 (1), 69–74. https://doi.org/10.1038/s41561-018-0272-8 Laurenti, L., Tinti, E., Galasso, F., Franco, L., & Marone, C. (2022). Deep learning for laboratory earthquake prediction and autoregressive forecasting of fault zone stress. Earth and Planetary Science Letters, Volume 598, 2022, 117825, ISSN 0012-821X, https://doi.org/10.1016/j.epsl.2022.117825 . Mastella G., Corbi F., Funiciello F., Rosenau M., (2022a): Foamquake a novel analog model mimicking megathrust seismic cycles; Journal of Geophysical Research: Solid Earth, https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022789. Mastella, G., Corbi, F., Bedford, J., Funiciello, F., & Rosenau, M. (2022b). Forecasting surface velocity fields associated with laboratory seismic cycles using Deep Learning. Geophysical Research Letters, 49,e2022GL099632. https://doi.org/10.1029/2022GL099632. Michel, S., Gualandi, A., & Avouac, J. P. (2019). Similar scaling laws for earthquakes and Cascadia slow-slip events. Nature , 574 (7779), 522–526. https://doi.org/10.1038/s41586-019-1673-6 . Philibosian, B., & Meltzner, A. J., (2020). Segmentation and supercycles: A catalog of earthquake rupture patterns from the Sumatran Sunda megathrust and other well-studied faults worldwide. https://doi.org/10.1016/j.quascirev.2020.106390 Ren, C. X., Hulbert, C., Johnson, P. A., & Rouet-Leduc, B. (2020). Machine learning and fault rupture: A review. Advances in Geophysics , 61 , 57–107. https://doi.org/10.1016/2Fbs.agph.2020.08.003. Rouet-Leduc, B., Hulbert, C., Bolton, D. C., Ren, C. X., Riviere, J., Marone, C., et al. (2018). Estimating fault friction from seismic signals in the laboratory. Geophysical Research Letters , 45 (3), 1321–1329. https://doi.org/10.1002/2F2017GL076708 Rouet-Leduc, B., Hulbert, C., Lubbers, N., Barros, K., Humphreys, C. J., & Johnson, P. A. (2017). Machine learning predicts laboratory earth- quakes. Geophysical Research Letters , 44 (18), 9276–9282. https://doi.org/10.1002/2F2017GL074677 . Van Klaveren, S., Vasconcelos, I., & Niemeijer, A. (2020). Predicting laboratory earthquakes with machine learning. arXiv preprint arXiv:2011.06669. Corresponding author: Giacomo Mastella (giacomo.mastella@uniroma3.it)