The Potential for Earthquake Early Warning in Southern California
By Richard M. Allen, Hiroo Kanamori. Science, 2003
616 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Geophysics
View Paper: https://www.semanticscholar.org/paper/c19fc34ead321c3dea02769b39f135aedb05e5fd
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In Artificial Intelligence
- Zachary E. Ross
- Jang-Tian Shieh
- S. Nielsen
- Yih‐Min Wu
- Maria Lancieri
- Aldo Zollo
- Jean-Paul Ampuero
- P. Martin Mai
In Geophysics
- Zachary E. Ross
- S. Nielsen
- Jang-Tian Shieh
- Yih‐Min Wu
- Maria Lancieri
- Aldo Zollo
- Jean-Paul Ampuero
- P. Martin Mai
Citations in the corpus
- Earthquake source characteristics from dynamic rupture with constrained stochastic fault stress (2007, methodology): “We take f * = 1 Hz after Allen and Kanamori [2003]. Shortly after slip has reached Dc, the dominant frequency Figure 4.”
- Earthquake magnitude estimation from peak amplitudes of very early seismic signals on strong motion records (2006, methodology): “[4] In order to provide real-time magnitude estimations for EEW purposes, a method based on the predominant period (tp) measured over a narrow time window (4 seconds) extending from just after the first P-wave arrival has been first proposed by Allen and Kanamori [2003] .”
- A comparison of τc and τpmax for magnitude estimation in earthquake early warning (2008, methodology): “Our results show that using two poles in the filters results in the best magnitude estimates, i.e., minimized the standard deviation in magnitude determination in comparison to Mw using tc. The tp parameters ( Allen and Kanamori, 2003 )”
- P Wave Arrival Picking and First‐Motion Polarity Determination With Deep Learning (2018, background): “In applications of real-time seismology, such as earthquake early warning (Allen & Kanamori, 2003; Heaton, 1985; Satriano et al., 2011), all inference is necessarily performed with automated algorithms.”