Statistical classification methods applied to seismic discrimination

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To verify compliance with a Comprehensive Test Ban Treaty (CTBT), low energy seismic activity must be detected and discriminated. Monitoring small-scale activity will require regional (within {approx}2000 km) monitoring capabilities. This report provides background information on various statistical classification methods and discusses the relevance of each method in the CTBT seismic discrimination setting. Criteria for classification method selection are explained and examples are given to illustrate several key issues. This report describes in more detail the issues and analyses that were initially outlined in a poster presentation at a recent American Geophysical Union (AGU) meeting. Section 2 of this report ... continued below

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37 p.

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Ryan, F.M.; Anderson, D.N.; Anderson, K.K.; Hagedorn, D.N.; Higbee, K.T.; Miller, N.E. et al. June 11, 1996.

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Description

To verify compliance with a Comprehensive Test Ban Treaty (CTBT), low energy seismic activity must be detected and discriminated. Monitoring small-scale activity will require regional (within {approx}2000 km) monitoring capabilities. This report provides background information on various statistical classification methods and discusses the relevance of each method in the CTBT seismic discrimination setting. Criteria for classification method selection are explained and examples are given to illustrate several key issues. This report describes in more detail the issues and analyses that were initially outlined in a poster presentation at a recent American Geophysical Union (AGU) meeting. Section 2 of this report describes both the CTBT seismic discrimination setting and the general statistical classification approach to this setting. Seismic data examples illustrate the importance of synergistically using multivariate data as well as the difficulties due to missing observations. Classification method selection criteria are presented and discussed in Section 3. These criteria are grouped into the broad classes of simplicity, robustness, applicability, and performance. Section 4 follows with a description of several statistical classification methods: linear discriminant analysis, quadratic discriminant analysis, variably regularized discriminant analysis, flexible discriminant analysis, logistic discriminant analysis, K-th Nearest Neighbor discrimination, kernel discrimination, and classification and regression tree discrimination. The advantages and disadvantages of these methods are summarized in Section 5.

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37 p.

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OSTI as DE96012316

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  • Other Information: PBD: 11 Jun 1996

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  • Other: DE96012316
  • Report No.: PNNL--11192
  • Grant Number: AC06-76RL01830
  • DOI: 10.2172/257361 | External Link
  • Office of Scientific & Technical Information Report Number: 257361
  • Archival Resource Key: ark:/67531/metadc672700

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Office of Scientific & Technical Information Technical Reports

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  • June 11, 1996

Added to The UNT Digital Library

  • June 29, 2015, 9:42 p.m.

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  • April 6, 2016, 2:56 p.m.

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Ryan, F.M.; Anderson, D.N.; Anderson, K.K.; Hagedorn, D.N.; Higbee, K.T.; Miller, N.E. et al. Statistical classification methods applied to seismic discrimination, report, June 11, 1996; Richland, Washington. (digital.library.unt.edu/ark:/67531/metadc672700/: accessed November 13, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.