Single-Component/Single-Station–Based Machine Learning for Estimating Magnitude and Location of an Earthquake: A Support Vector Machine Approach
Author:
Funder
Ministry of Earth Sciences, India
Publisher
Springer Science and Business Media LLC
Subject
Geochemistry and Petrology,Geophysics
Link
https://link.springer.com/content/pdf/10.1007/s00024-021-02745-8.pdf
Reference33 articles.
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2. Asim, K. M., Javed, F., Hainzl, S., & Iqbal, T. (2019). Fault parameters-based earthquake magnitude estimation using artificial neural networks. Seismological Research Letters, 90(4), 1544–1551. https://doi.org/10.1785/0220190051
3. Audretsch, J. (2020). Earthquake Detection using Deep Learning Based Approaches (Thesis). https://doi.org/10.25781/KAUST-52098
4. Bellagamba, X., Lee, R., & Bradley, B. A. (2019). A neural network for automated quality screening of ground motion records from small magnitude earthquakes. Earthquake Spectra, 35(4), 1637–1661. https://doi.org/10.1193/122118EQS292M
5. Bergen, K. J., Chen, T., & Li, Z. (2019). Preface to the focus section on machine learning in seismology. Seismological Research Letters, 90(2A), 477–480. https://doi.org/10.1785/0220190018
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