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Abstract

Summary

Attaining information of the source mechanism involved in micro-seismic events will greatly help us understand the reservoir fracturing and the stress evolved. The components of moment tensor can tell us the information involving magnitudes, modes, and orientations of fractures. Meanwhile, its singular value decomposition (SVD) exposes the difference between three main kinds of source types that may present itself in a moment tensor solution. We propose to use support vector machine (SVM), which is a type of machine learning approach, to classify the source type of a micro-seismic event by using the normalized eigenvalues of moment tensor matrix as classification principal components. The tests on moment tensor matrices based on typical source type and real cases yield reliable classification results.

© EAGE Publications BV
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/content/papers/10.3997/2214-4609.201801577
2018-06-11
2024-04-19

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References

  1. Aki, K. and Richards, P.
    [2002] Quantitative seismology, 2nd ed. University Science Books.
     [Google Scholar]
  2. Al-Anazi, A. and Gates, I.
    [2010] A support vector machine algorithm to classify lithofacies and model permeability in heterogeneous reservoirs. Engineering Geology, 267–277.
     [Google Scholar]
  3. Burges, C.
    [1998] A tutorial on support vector machines for pattern recognition. Kluwer Academic Publishers, Boston.
     [Google Scholar]
  4. Eyre, T.S. and Baan, M.
    [2015] Overview of moment-tensor inversion of microseismic event. The leading edge. 882–888.
     [Google Scholar]
  5. Fletcher, R.
    [1987] Practical Methods of Optimization, 2nd ed. John Wiley, New York.
     [Google Scholar]
  6. Kovaĉević, M., Bajat, B. and Gajic, B.
    [2009] Soil type classification and estimation of soil properties using support vector machines. Geoderma, 340–347.
     [Google Scholar]
  7. Li, J. and Castagna, J.
    [2004] Support vector machine (SVM) pattern recgonition to AVO classification. Geophysical Research Letters. L02609.
     [Google Scholar]
  8. Vapnik, V.
    [1995] The Nature of Statistical Learning Theory. Springer, New York.
     [Google Scholar]
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Source Type Classification Based On the Support Vector Machine Method
Conference Proceedings 2018, 1 (2018); https://doi.org/10.3997/2214-4609.201801577
/content/papers/10.3997/2214-4609.201801577
/content/papers/10.3997/2214-4609.201801577
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