Probabilistic forecasting of earthquakes

By Y. Kagan, D. Jackson. 2000

308 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Geophysics

View Paper: https://www.semanticscholar.org/paper/feabd299378c96855b5b8b386e1ad46dfbc600a3

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Citations in the corpus

Subcritical and supercritical regimes in epidemic models of earthquake aftershocks (2001, methodology): “2”
Seismic moment distribution revisited: II. Moment conservation principle (2002, background): “This distribution is expected to take into account, possibly incompletely, the short-term earthquake clustering, i.e. foreshock–main-shock–aftershock sequences (Kagan & Jackson 2000).”
Probabilistic Analysis of Tsunami Hazards* (2006, background): “R distribution of Kagan and Jackson (2000) (see also Vere-Jones et al., 2001).”
Discrimination ability of the Energy score (2013, background): “This phenomenon touches a wide range of applications, from economics and finance [1, 2], to earthquake prediction [3], while it also has wide appeal in meteorology [4], and for weather-related processes like renewable energy production [5, 6] and floods [7].”
Tohoku earthquake: a surprise? (2011, background): “Several quantitative estimates of the maximum possible earthquakes in the subduction zones had been published before the Tohoku event (Kagan, 1997; Kagan and Jackson, 2000; Bird and Kagan, 2004; McCaffrey, 2007, 2008; Kagan et al., 2010).”
Distributional properties of the negative binomial Lévy process (2009, background): “Applications of NB models include theory of accidents ([3], [46], [54], [63], [76]), population growth processes and epidemiology ([49], [70], [79], [80], [84], [89], [108]), particle physics ([4], [26], [29], [34], [49], [90]), geosciences ([67], [71]), cosmology [25], psychology [98], economics [1”
Foreshocks explained by cascades of triggered seismicity (2002, methodology): “The ETAS model has been used previously to give short-term probabilistic forecast of seismic activity [Kagan and Knopoff, 1987; Kagan and Jackson, 2000; Console and Murru, 2001], and to describe the temporal and spatial clustering of seismic activity [Ogata, 1988; Kagan, 1991; Kagan and Jackson,…”
Very‐short‐term probabilistic forecasting of wind power with generalized logit–normal distributions (2012, methodology): “This has been the case in economics and finance (Tay and Wallis 2000, Timmermann 2000), earthquake prediction (Kagan and Jackson 2000), meteorology (Leutbecher and Palmer 2008), as well as for weather-related processes like renewable energy production (Bacher et al.”
Aftershock Zone Scaling (2002, background): “Kanamori and Anderson (1975), Geller (1976), and Sato (1979) proposed that for small and moderate earthquakes the scalar moment M is proportional to the cube of its focal length l”
Comparative performance of time‐invariant, long‐range and short‐range forecasting models on the earthquake catalogue of Greece (2006, methodology): “Another possibility given by such formulation is the computation of the likelihood ratio (performance factor) of two models based on a given catalogue, as a measure of their relative information value, with one model considered as a null hypothesis [Rhoades and Evison, 1979, 1989; Evison and Rhoades”
Connecting near-field and far-field earthquake triggering to dynamic strain (2010, methodology): “This is a standard model for aftershock forecasting and is frequently used to analyze the significance of earthquake triggering metrics [Kagan and Jackson, 2000;Marsan, 2003; Matthews and Reasenberg, 1988; Ogata, 1999; Reasenberg and Jones, 1989; Wiemer, 2000].”
Seismic moment distribution revisited: I. Statistical results (2002, background): “2 in Kagan & Jackson 2000), the comparison demonstrates that with a lower mt value we can better estimate b.”