The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.05 - Sept.-Oct. (2012 vol.14)
pp: 31-42
Andrea Donnellan , Jet Propulsion Laboratory, NASA
Jay Parker , Jet Propulsion Laboratory, NASA
Margaret Glasscoe , Jet Propulsion Laboratory, NASA
Eric De Jong , Jet Propulsion Laboratory, NASA
Marlon Pierce , Indiana University
Geoffrey Fox , Indiana University
Dennis McLeod , University of Southern California
John Rundle , University of California, Davis
Lisa Grant Ludwig , University of California, Irvine
ABSTRACT
Advances in understanding earthquakes require the integration of models and multiple distributed data products. Increasingly, data are acquired through large investments, and utilizing their full potential requires a coordinated effort by many users, independent researchers, and groups who are often distributed both geographically and by expertise.
INDEX TERMS
Earthquakes, Data models, Computational modeling, Global Positioning System, Analytical models, Time series analysis, Deformable models, Predictive models, scientific computing, Web services interoperability, Earth and atmospheric sciences, computational earthquake science
CITATION
Andrea Donnellan, Jay Parker, Margaret Glasscoe, Eric De Jong, Marlon Pierce, Geoffrey Fox, Dennis McLeod, John Rundle, Lisa Grant Ludwig, "A Distributed Approach to Computational Earthquake Science: Opportunities and Challenges", Computing in Science & Engineering, vol.14, no. 5, pp. 31-42, Sept.-Oct. 2012, doi:10.1109/MCSE.2012.59
REFERENCES
1. J.R. Holliday et al., “A RELM Earthquake Forecast Based on Pattern Informatics,” Seismological Research Letters, vol. 78, no. 1, 2007, pp. 87–93.
2. Y.-T. Lee et al., “Results of the Regional Earthquake Likelihood Models (RELM) Test of Earthquake Forecasts in California,” Proc. Nat'l Academy of Sciences USA, vol. 108, 2011, pp. 16533–16538; doi:10.1073/pnas.1113481108.
3. T.H. Jordan and L. Jones, “Operational Earthquake Forecasting: Some Thoughts on Why and How,” Seismological Research Letters, vol. 81, no. 4, 2010, pp. 571–574.
4. E.H. Field et al., The Uniform California Earthquake Rupture Forecast, Version 2 (UCERF-2), USGS Open File Report 2007-1437, CGS Special Report 203, SCEC Contribution 1138, 2007 Working Group on California Earthquake Probabilities, 2008; http://pubs.usgs.gov/of/20071437.
5. H. Iizuka, Y. Sakai, and K. Koketsu, “Strong Ground Motions and Damage Conditions Associated with Seismic Stations in the February 2011 Christchurch, New Zealand Earthquake,” Seismological Research Letters, vol. 82, no. 6, 2011, pp. 875–881.
6. A. Donnellan, J.W. Parker, and G. Peltzer, “Combined GPS and InSAR Models of Postseismic Deformation from the Northridge Earthquake,” Pure and Applied Geophysics, vol. 159, no. 10, 2002, pp. 2261–2270.
7. M. Wei, D. Sandwell, and B. Smith-Konter, “Optimal Combination of InSAR and GPS for Measuring Interseismic Crustal Deformation,” J. Advances in Space Research, vol. 46, no. 2, 2010, pp. 236–249; doi:10.1016/j.asr.2010.03.013.
8. Service Aggregated Linked Sequential Activities (SALSA) Group, Twister Iterative MapReduce, 2010; www.iterativemapreduce.org.
9. J. Ekanayake et al., “Twister: A Runtime for Iterative MapReduce,” Proc. ACM Int'l Symp. High-Performance Parallel and Distributed Computing, 2010, ACM; http://grids.ucs.indiana.edu/ptliupages/ publicationshpdc-camera-ready-submission.pdf .
10. B. Zhang et al., “Applying Twister to Scientific Applications,” Proc. IEEE 2nd Int'l Conf. Cloud Computing Technology and Science, IEEE CS, 2010, pp. 23–52; http://grids.ucs.indiana.edu/ptliupages/ publicationsPID1510523.pdf.
11. T. Gunarathne, J. Qiu, and G. Fox, “Iterative MapReduce for Azure Cloud,” Proc. Cloud Computing and Its Applications, ACM, 2011; http://grids.ucs.indiana.edu/ptliupages/ publicationscca_v8.pdf.
41 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool