About this Journal Submit a Manuscript Table of Contents
Abstract and Applied Analysis
Volume 2014 (2014), Article ID 459137, 8 pages
http://dx.doi.org/10.1155/2014/459137
Research Article

Nonlinear Methodologies for Identifying Seismic Event and Nuclear Explosion Using Random Forest, Support Vector Machine, and Naive Bayes Classification

1School of Resources and Safety Engineering, Central South University, Changsha 410083, China
2School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, 710129, China

Received 26 December 2013; Accepted 16 January 2014; Published 26 February 2014

Academic Editor: Carlo Cattani

Copyright © 2014 Longjun Dong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. L. Dong and X. Li, “A microseismic/acoustic emission source location method using arrival times of PS waves for unknown velocity system,” International Journal of Distributed Sensor Networks, vol. 2013, Article ID 307489, 8 pages, 2013. View at Publisher · View at Google Scholar
  2. L. Dong and X. Li, “Three-dimensional analytical solution of acoustic emission or microseismic source location under cube monitoring network,” Transactions of Nonferrous Metals Society of China, vol. 22, no. 12, pp. 3087–3094, 2012.
  3. X. B. Li and L. J. Dong, “Comparison of two methods in acoustic emission source location using four sensors without measuring sonic speed,” Sensor Letters, vol. 9, no. 5, pp. 2025–2029, 2011. View at Publisher · View at Google Scholar · View at Scopus
  4. L. Dong and X. Li, “Hypocenter relocation for Wenchuan Ms 8. 0 and Lushan Ms 7. 0 earthquakes using TT and TD methods,” Disaster Advances, vol. 6, no. 13, pp. 304–313, 2013.
  5. Y. Gitterman, V. Pinsky, and A. Shapira, “Spectral discrimination analysis of Eurasian nuclear tests and earthquakes recorded by the Israel Seismic Network and the NORESS array,” Physics of the Earth and Planetary Interiors, vol. 113, no. 1–4, pp. 111–129, 1999. View at Publisher · View at Google Scholar · View at Scopus
  6. S. J. Arrowsmith, M. D. Arrowsmith, M. A. H. Hedlin, and B. Stump, “Discrimination of delay-fired mine blasts in Wyoming using an automatic time-frequency discriminant,” Bulletin of the Seismological Society of America, vol. 96, no. 6, pp. 2368–2382, 2006. View at Publisher · View at Google Scholar · View at Scopus
  7. A. J. Mendecki, Seismic Monitoring in Mines, Chapman & Hall, 1996.
  8. J. Wuster, “Discrimination of chemical explosions and earthquakes in central Europe—a case study,” Bulletin of the Seismological Society of America, vol. 83, no. 4, pp. 1184–1212, 1993. View at Scopus
  9. R. Blandford, “Discrimination between earthquakes and underground explosions,” Annual Review of Earth and Planetary Sciences, vol. 5, p. 111, 1977.
  10. A. T. Smith, “Discrimination of explosions from simultaneous mining blasts,” Bulletin of the Seismological Society of America, vol. 83, no. 1, pp. 160–179, 1993. View at Scopus
  11. D. R. Baumgardt and G. B. Young, “Regional seismic waveform discriminants and case-based event identification using regional arrays,” Bulletin of the Seismological Society of America, vol. 80, no. 6, pp. 1874–1892, 1990. View at Scopus
  12. S. R. Taylor, M. D. Denny, E. S. Vergino, and R. E. Glaser, “Regional discrimination between NTS explosions and western US earthquakes,” Bulletin of the Seismological Society of America, vol. 79, no. 4, pp. 1142–1176, 1989. View at Scopus
  13. S. Taylor, M. Denny, and E. Vergino, “Regional m/sub b: M/sub s/discrimination of NTS explosions and western United States earthquakes,” Progress Report, Lawrence Livermore National Laboratory, Livermore, Calif, USA, 1986.
  14. S. G. Kim, Y. Park, and W. Kim, “Discrimination of small earthquakes and artificial explosions in the Korean Peninsula using Pg/Lg ratios,” Geophysical Journal International, vol. 134, no. 1, pp. 267–276, 1998. View at Publisher · View at Google Scholar · View at Scopus
  15. D. R. Baumgardt and K. A. Ziegler, “Spectral evidence for source multiplicity in explosions: application to regional discrimination of earthquakes and explosions,” Bulletin of the Seismological Society of America, vol. 78, no. 5, pp. 1773–1795, 1988. View at Scopus
  16. M. A. H. Hedlin, J. B. Minster, and J. A. Orcutt, “An automatic means to discriminate between earthquakes and quarry blasts,” Bulletin of the Seismological Society of America, vol. 80, no. 6, pp. 2143–2160, 1990. View at Scopus
  17. Y. Gitterman and T. van Eck, “Spectra of quarry blasts and microearthquakes recorded at local distances in Israel,” Bulletin of the Seismological Society of America, vol. 83, no. 6, pp. 1799–1812, 1993. View at Scopus
  18. A. Booker and W. Mitronovas, “An application of statistical discrimination to classify seismic events,” Bulletin of the Seismological Society of America, vol. 54, no. 3, pp. 961–971, 1964.
  19. L. Dong, X. Li, C. Ma, and W. Zhu, “Comparisons of Logistic regression and Fisher discriminant classifier to seismic event identification,” Disaster Advances, vol. 6, supplement 4, pp. 1–8, 2013.
  20. L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  21. V. F. Rodriguez-Galiano, B. Ghimire, J. Rogan, M. Chica-Olmo, and J. P. Rigol-Sanchez, “An assessment of the effectiveness of a random forest classifier for land-cover classification,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 67, no. 1, pp. 93–104, 2012. View at Publisher · View at Google Scholar · View at Scopus
  22. P. M. Granitto, F. Gasperi, F. Biasioli, E. Trainotti, and C. Furlanello, “Modern data mining tools in descriptive sensory analysis: a case study with a Random forest approach,” Food Quality and Preference, vol. 18, no. 4, pp. 681–689, 2007. View at Publisher · View at Google Scholar · View at Scopus
  23. R. Genuer, J. Poggi, and C. Tuleau-Malot, “Variable selection using random forests,” Pattern Recognition Letters, vol. 31, no. 14, pp. 2225–2236, 2010. View at Publisher · View at Google Scholar · View at Scopus
  24. L. J. Dong, X. B. Li, and K. Peng, “Prediction of rockburst classification using Random Forest,” Transactions of Nonferrous Metals Society of China, vol. 23, no. 2, pp. 472–477, 2013.
  25. L. Dong and X. Li, “Comprehensive models for evaluating rockmass stability based on statistical comparisons of multiple classifiers,” Mathematical Problems in Engineering, vol. 2013, Article ID 395096, 10 pages, 2013. View at Publisher · View at Google Scholar
  26. L. J. Dong, X. B. Li, M. Xu, and Q. Li, “Comparisons of random forest and Support Vector Machine for predicting blasting vibration characteristic parameters,” Procedia Engineering, vol. 26, pp. 1772–1781, 2011.
  27. C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, no. 3, pp. 273–297, 1995. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  28. M. E. Maron, “Automatic indexing: an experimental inquiry,” Journal of the ACM, vol. 8, no. 3, pp. 404–417, 1961. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  29. I. Kononenko, “Inductive and Bayesian learning in medical diagnosis,” Applied Artificial Intelligence, vol. 7, no. 4, pp. 317–337, 1993. View at Scopus
  30. P. Berchialla, F. Foltran, and D. Gregori, “Naïve Bayes classifiers with feature selection to predict hospitalization and complications due to objects swallowing and ingestion among European children,” Safety Science, vol. 51, no. 1, pp. 1–5, 2013.
  31. R. Powers, M. Goldszmidt, and I. Cohen, “Short term performance forecasting in enterprise systems,” in Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '05), pp. 801–807, ACM, August 2005. View at Publisher · View at Google Scholar · View at Scopus
  32. Y. Fu and L. Dong, “Bayes discriminant analysis model and its application to the prediction and classification of rockburst,” Journal of China University of Mining and Technology, vol. 38, no. 4, pp. 56–64, 2009. View at Scopus
  33. P. Domingos and M. Pazzani, “On the optimality of the simple Bayesian classifier under zero-one loss,” Machine Learning, vol. 29, no. 2-3, pp. 103–130, 1997. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  34. A. P. Bradley, “The use of the area under the ROC curve in the evaluation of machine learning algorithms,” Pattern Recognition, vol. 30, no. 7, pp. 1145–1159, 1997. View at Scopus