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Porting a Seismic Network to the Grid

  • Paolo Gamba
  • Matteo Lanati
Conference paper

Abstract

The chapter describes the experience and lessons learnt during customization of a seismic early warning system for the grid technology. Our goal is to shorten the workflow of an experiment, so that final users have direct access to data sources, i.e. seismic sensors, without intermediaries and without leaving the environment employed for the analysis. We strongly rely on remote instrumentation capabilities of the grid, a feature that makes this platform very attractive for scientific communities aiming at blending computational procedures and data access in a single tool. The expected outcome should be a distributed virtual laboratory working in a secure way regardless of the distance or the number of participants. We started to set up the application and the infrastructure as a part of the DORII (Deployment of Remote Instrumentation Infrastructure) project. In the following sections we will try to explain the steps that led us to integration, the experience perceived by the testers, the results obtained so far and future perspectives.

Keywords

Response Spectrum Early Warning System Finite State Machine Grid Service Connection Request 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The work described is carried out under the DORII project, supported by the European Commission with contract number 213110. The authors would like to thank Mirko Corigliano for the analysis software ported into the grid and for the related explanations. The cooperation of Dip.Te.Ris at University of Genoa was fundamental to access the sensor network. In particular, the contribution received from Daniele Spallarossa and Gabriele Ferretti was very useful to fix the application. Finally, the work of Davide Silvestri in implementing the Instrument Managers and part of the Java library was highly appreciated.

References

  1. 1.
    Y. Guo, J. G. Liu, M. Ghanem, K. Mish, V. Curcin, C. Haselwimmer, D. Sotiriou, K.K Muraleetharan, L. Taylor, “Bridging the Macro and Micro: A Computing Intensive Earthquake Study Using Discovery Net”, Proc. of the 2005 ACM/IEEE SC 05 Conference (SC 05), Seattle, WA, USA, Nov. 2005.Google Scholar
  2. 2.
    A. Donnellan, J. Parker, C. Norton, G. Lyzenga, M. Glasscoe,, M. Glasscoe, G. Fox, M. Pierce, J. Rundle, D. McLeod, L. Grant, W. Brooks, T. Tullis “QuakeSim: Enabling Model Interactions in Solid Earth Science Sensor Webs”, Proc. of 2007 IEE Aerospace Conference, Big Sky, MT, USA, Mar. 2007.Google Scholar
  3. 3.
    R. Ranon, L. De Marco, A. Senerchia, S. Gabrielli, L. Chittaro, R. Pugliese, L. Del Cano, F. Asnicar, M. Prica, “A web-based tool for collaborative access to scientific instruments in cyberinfrastructures” in F. Davoli, N. Meyer, R. Pugliese, S. Zappatore, Eds., Grid Enabled Remote Instrumentation, Springer, New York, NY, 2008, pp. 237-251.Google Scholar
  4. 4.
    E. Frizziero, M. Gulmini, F. Lelli, G. Maron, A. Oh, S. Orlando, A. Petrucci, S. Squizzato, S. Traldi, “Instrument Element: a new Grid component that enables the control of remote instrumentation” Proc. 6th IEEE Internat. Symp. on Cluster Computing and the Grid Workshops (CCGRIDW 06), Singapore, May 2006.Google Scholar
  5. 5.
    N. M. Newmark, “A Method of Computation for Structural Dynamics”, ASCE Journal of the Engineering Mechanics Division, Vol. 85, No. 3, 1959, pp. 67-94.Google Scholar
  6. 6.
    M. Abramowitz, I. A. Stegun, “Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables”, Dover, New York, NY, 1964, Section 25.3.41.Google Scholar
  7. 7.
    A. K. Chopra, “Dynamics of Structures (Theory and Applications to Earthquake), 3rd Edition”, Prentice Hall, Upper Saddle River, NJ, 2007, pp. 208-217.Google Scholar
  8. 8.
    Nanometrics Data Formats - Reference Guide.Google Scholar
  9. 9.
    M. Okoń, D. Kaliszan, M. Lawenda, D. Stokłosa, T. Rajtar, N. Meyer, M. Stroiński, “Virtual Laboratory as a remote and interactive access to the scientific instrumentation embedded in Grid environment”, Proc. 2nd IEEE Internat. Conf. on e-Science and Grid Computing (e-Science 06), Amsterdam, The Netherlands, Dec. 2006.Google Scholar
  10. 10.
    H. Zhang, “Application of Multilayer Perceptron (MLP) Neural Network in Identification and Picking P-wave arrival”, ECE539 Project Report, Department of Geology and Geophysics, University of Wisconsin-Madison, 2001.Google Scholar
  11. 11.
    H. Akaike, “Markovian representation of stochastic processes and its application to the analysis of autoregressive moving average process”, Annals of the Institute of Statistical Mathematics, Vol. 26, 1974, pp. 363-387.CrossRefMATHMathSciNetGoogle Scholar
  12. 12.
    R. Sleeman, T. V. Eck, “Robust automatic P-phase picking: an on-line implementation in the analysis of broadband seismogram recordings”, Physics of the Earth and Planetary Interiors, Vol. 113, 1999, pp. 265-275.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of ElectronicsUniversity of PaviaPaviaItaly
  2. 2.EucentrePaviaItaly

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