Revised Empirical Relations Between Earthquake Source and Rupture Parameters by Regression and Machine Learning Algorithms
Author:
Publisher
Springer Science and Business Media LLC
Subject
Geochemistry and Petrology,Geophysics
Link
https://link.springer.com/content/pdf/10.1007/s00024-023-03340-9.pdf
Reference67 articles.
1. Acharya, H. K. (1979). Regional variations in the rupture-length magnitude relationships and their dynamical significance. Bulletin of the Seismological Society of America, 69, 2063–2084.
2. Asim, K. M., Idris, A., Iqbal, T., & Martínez-Álvarez, F. (2018). Earthquake prediction model using support vector regressor and hybrid neural networks. PLoS One, 13, e0199004. https://doi.org/10.1371/journal.pone.0199004
3. Asim, K. M., Javed, F., Hainzl, S., & Iqbal, T. (2019). Fault parameters-based earthquake magnitude estimation using artificial neural networks. Seismological Research Letters, 90, 1544–1551. https://doi.org/10.1785/0220190051
4. Atkinson, G. M., & Boore, D. M. (2003). Empirical ground-motion relations for subduction-zone earthquakes and their application to cascadia and other regions. Bulletin of the Seismological Society of America, 93, 1703–1729. https://doi.org/10.1785/0120020156
5. Atzori, S., Antonioli, A., Tolomei, C., De Novellis, V., De Luca, C., & Monterroso, F. (2019). InSAR full-resolution analysis of the 2017–2018 M>6 earthquakes in Mexico. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2019.111461
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