Support Vector Machine-Based On-Site Prediction for China Seismic Instrumental Intensity from P-Wave Features-Reference-Cited by-同舟云学术

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Support Vector Machine-Based On-Site Prediction for China Seismic Instrumental Intensity from P-Wave Features

Published:2023-09-05 Issue:10 Volume:180 Page:3495-3515
ISSN:0033-4553
Container-title:Pure and Applied Geophysics
language:en
Short-container-title:Pure Appl. Geophys.

Author:

Hou Baorui^ORCID,Li Shanyou,Song Jindong^ORCID

Funder

National Key Research and Development Program of China

Scientific Research Fund of Institute of Engineering Mechanics, China Earthquake Administration

Natural Science Foundation of Heilongjiang Province

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Geochemistry and Petrology,Geophysics

Link

https://link.springer.com/content/pdf/10.1007/s00024-023-03335-6.pdf

Reference50 articles.

1. Akaike, H. (1998). Information theory and an extension of the maximum likelihood principle. In E. Parzen, K. Tanabe, & G. Kitagawa (Eds.), Selected papers of hirotugu akaike (pp. 199–213). Springer.

2. Allen, R. V. (1978). Automatic earthquake recognition and timing from single traces. Bulletin of the Seismological Society of America, 68(5), 1521–1532. https://doi.org/10.1785/bssa0680051521

3. Allen, R. V. (1982). Automatic phase pickers: Their present use and future prospects. Bulletin of the Seismological Society of America, 72(6B), S225–S242. https://doi.org/10.1785/bssa07206b0225

4. Allen, R. M., Gasparini, P., Kamigaichi, O., & Bose, M. (2009). The status of earthquake early warning around the world: An introductory overview. Seismological Research Letters, 80(5), 682–693.

5. Allen, R. M., & Melgar, D. (2019). Earthquake early warning: Advances, scientific challenges, and societal needs. Annual Review of Earth and Planetary Sciences, 47(1), 361–388. https://doi.org/10.1146/annurev-earth-053018-060457

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