Analyzing earthquake clustering features by using stochastic reconstruction
By Jiancang Zhuang, Yosihiko Ogata, David Vere-Jones. 2004
330 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Geophysics
View Paper: https://www.semanticscholar.org/paper/65286f7ac72ca41ee6ff40e77559bc96e86cd9c7
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Agnes Helmstetter
- Y. Kagan
- Yosihiko Ogata
- Jiancang Zhuang
- Sebastian Hainzl
- D. Jackson
- C. Beauval
- Frank Scherbaum
In Geophysics
- Agnes Helmstetter
- D. Jackson
- Y. Kagan
- Yosihiko Ogata
- Jiancang Zhuang
- Sebastian Hainzl
- Frank Scherbaum
- C. Beauval
Citations in the corpus
- Estimating Background Activity Based on Interevent-Time Distribution (2006, methodology): “"Rm where q " 1.69 is chosen according to the inverted value given by Zhuang et al. (2004) and Rm " 0.011 • 10 0.5"Mm (km) is the assumed extension of a mainshock rupture with magnitude Mm (Reasenberg, 1985).”
- Second‐order residual analysis of spatiotemporal point processes and applications in model evaluation (2006, methodology): “model (Zhuang et al., 2002, 2004) (a) Set up the initial background seismicity rate; for example, let û.x/=1.”
- Importance of small earthquakes for stress transfers and earthquake triggering (2004, methodology): “Only a stochastic answer can be obtained, assuming a particular model of earthquake interactions: for example, that earthquakes obey the Epidemic Type Earthquake Sequences (ETES or ETAS) model [ Zhuang et al., 2004].”
- Selecting critical features for data classification based on machine learning methods (2020, methodology): “…[37, 38], landslide [39], earthquake damage detection using curvilinear features [40], earthquake classifiers using stochastic reconstruction [41] and tsunami [42] Health Future genetic association studies of colorectal cancer [11], Aortic Anatomy on Endovascular Aneurysm Repair (EVAR) [43],…”
- Space–time ETAS models and an improved extension (2006, methodology): “In order to examine the appropriateness of the function form exp{a(M Mc)} /d in the model (7), Zhuang et al. (2004) calculated the distance ri,j between a triggered event j and its direct ancestor, event i, that belong to a given magnitude band Mia DM, using the set of clusters that are declustered…”
- Prediction (2010, neutral): “These results have been obtained by rigo rous mathematical derivations using probability generating functions: • non-mean field anomalous exponents for the distribution of “ cluster” sizes due to the interplay between cascades of generation and the power laws of productivity and of marks (magnitudes”