Real-time discrimination of earthquake foreshocks and aftershocks
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DOI: 10.1038/s41586-019-1606-4
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Cited by:
- Matteo Picozzi & Antonio Giovanni Iaccarino, 2021. "Forecasting the Preparatory Phase of Induced Earthquakes by Recurrent Neural Network," Forecasting, MDPI, vol. 3(1), pages 1-20, January.
- Satoshi Matsumoto & Yoshihisa Iio & Shinichi Sakai & Aitaro Kato, 2024. "Strength dependency of frequency–magnitude distribution in earthquakes and implications for stress state criticality," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
- Elio Roca-Flores & Gerardo G. Naumis, 2021. "Assessing statistical hurricane risks: nonlinear regression and time-window analysis of North Atlantic annual accumulated cyclonic energy rank profile," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 108(3), pages 2455-2465, September.
- Matteo Taroni & Giorgio Vocalelli & Andrea De Polis, 2021. "Gutenberg–Richter B-Value Time Series Forecasting: A Weighted Likelihood Approach," Forecasting, MDPI, vol. 3(3), pages 1-9, August.
- Marcus Herrmann & Ester Piegari & Warner Marzocchi, 2022. "Revealing the spatiotemporal complexity of the magnitude distribution and b-value during an earthquake sequence," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
- C. Collettini & M. R. Barchi & N. Paola & F. Trippetta & E. Tinti, 2022. "Rock and fault rheology explain differences between on fault and distributed seismicity," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
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