Type of Document Master's Thesis Author Moncada de la Rosa, Jorge Daniel Author's Email Address email@example.com URN etd-12152008-083601 Title Application of Fluid Inclusions and Mineral Textures in Exploration for Epithermal Precious Metals Deposits Degree Master of Science Department Geosciences Advisory Committee
Advisor Name Title Bodnar, Robert J. Committee Co-Chair Nieto, Antonio V. Committee Co-Chair Rimstidt, james Donald Committee Member Keywords
- binary classifier statistical software
- epithermal precious metals deposits
- mineral exploration
- lattice-bladed calcite
- colloform silica
- fluid inclusions
- Veta Madre
Date of Defense 2008-12-09 Availability unrestricted Abstract
Fluid inclusion and mineralogical features indicative of boiling have been characterized in 855 samples from epithermal precious metals deposits along the Veta Madre at Guanajuato, Mexico. Features associated with boiling that have been identified at Guanajuato include colloform texture silica, plumose texture silica, moss texture silica, ghost-sphere texture silica, lattice-bladed calcite, lattice-bladed calcite replaced by quartz and pseudo-acicular quartz after calcite and coexisting liquid-rich and vapor-rich fluid inclusions. Most samples were assayed for Au, Ag, Cu, Pb, Zn, As and Sb, and were divided into high-grade and low-grade samples based on the gold and silver concentrations. For silver, the cutoff for high grade was 100 ppm Ag, and for gold the cutoff was 1 ppm Au. The feature that is most closely associated with high grades of both gold and silver is colloform texture silica, and this feature also shows the largest difference in grade between the presence or absence of that feature (178.8 ppm Ag versus 17.2 ppm Ag, and 1.1 ppm Au versus 0.2 ppm Au). For both Ag and Au, there is no significant difference in average grade as a function of whether or not coexisting liquid-rich and vapor-rich fluid inclusions are present.
The textural and fluid inclusion data obtained in this study were analyzed using the binary classifier within SPSS Clementine. The models that correctly predicted high versus low grade samples most consistently (≈70-75% of the tests) for both Ag and Au were the neural network, the C5 decision tree and Quest decision tree models. For both Au and Ag, the presence of colloform silica texture was the variable with the greatest importance, i.e., the variable that has the greatest predictive power.
Boiling features are absent or rare in samples collected along a traverse perpendicular to the Veta Madre. This suggests that if an explorationist observes these features in samples collected during exploration that an environment favorable to precious metal mineralization is nearby. Similarly, good evidence for boiling is observed in the deepest levels of the Veta Madre that have been sampled in the mines and drill cores, suggesting that additional precious metal reserves are likely beneath the deepest levels sampled.
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