ABSTRACT

ABSTRACT: This paper presents ongoing research in improvement of analytical tools for the characterization of rock mass structure from oriented borehole discontinuity data. Using automated multivariate cluster analysis and the "three dimensional stereo net" provides for fast and objective characterization of oriented core discontinuity data. Improvements to the CYL software include three new methods of cluster analysis, incorporation of roughness as well as orientation and spacing as variables, additional visualization modes, and an automated method to split the data set into different Geotechnical Mapping Units (GMU). In addition the program facilitates the selection of optimum drilling angle for boreholes.

Keywords:
orientation, roughness, Artificial Intelligence, Pattern Recognition, Upstream Oil & Gas, Euclidean Distance, machine learning, discontinuity data analysis, quantization method, bias index
Subjects:
Information Management and Systems, Artificial intelligence
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2000. A.A. Balkema - Permission to Distribute - American Rock Mechanics Association
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