Nickeliferous Mineral Deposit, Emory Creek, Yale Mining Division, British Columbia

1925 ◽  
Author(s):  
C E Cairnes
Keyword(s):  
British Columbia ◽  
Mineral Deposit ◽  
Mining Division

The thickness of Neogene and Quaternary cover across the central Interior Plateau, British Columbia: analysis of water-well drill records and implications for mineral exploration potential1This article is one of a series of papers published in this Special Issue on the theme of New insights in Cordilleran Intermontane geoscience: reducing exploration risk in the mountain pine beetle-affected area, British Columbia. 2Geological Survey of Canada (GSC) Contribution 20100036; Mineral Deposit Research Unit (MDRU, Department of Earh and Ocean Sciences, The University of British Columbia) Contribution p-261.

2011 ◽  
Vol 48 (6) ◽  
pp. 973-986 ◽  
Author(s):  
Graham D.M. Andrews ◽  
Alain Plouffe ◽  
Travis Ferbey ◽  
James K. Russell ◽  
Sarah R. Brown ◽  
...  
Keyword(s):  
British Columbia ◽  
Mountain Pine Beetle ◽  
Mineral Exploration ◽  
Three Dimensional ◽  
Research Unit ◽  
Mineral Deposit ◽  
Basic Knowledge ◽  
Data Sets ◽  
University Of British Columbia ◽  
Water Well

Analysis of over 10 000 water-well records has been used to produce new depth-to-bedrock maps for areas around five cities on the central Interior Plateau of central British Columbia: 100 Mile House, Prince George, Quesnel, Vanderhoof, and Williams Lake. Hitherto, exploration for mineral and hydrocarbon resources has been hampered by a lack of basic knowledge of the thickness of Neogene and Quaternary lithologies. Interpretation of these new maps provides first-order constraints on the localization of thick drift in pre-Late Wisconsinan bedrock paleovalleys, some of which are now buried. Basalt lavas of the Chilcotin Group are restricted to erosional remnants of previously extensive sheets emplaced onto an older peneplain. Our results confirm that the Neogene and Quaternary cover is primarily controlled by paleotopography and is generally thin and patchy across much of the region. Increased understanding of the three-dimensional distribution of cover produces a corresponding increase in the utility of geological, geochemical, and geophysical exploration techniques, and a reduction in the risk for future mineral exploration activities, especially when combined with more sophisticated data sets.

Mineral-resource prediction using advanced data analytics and machine learning of the QUEST-South stream-sediment geochemical data, southwestern British Columbia, Canada

2020 ◽  
Vol 21 (1) ◽  
pp. geochem2020-054 ◽  
Author(s):  
E. C. Grunsky ◽  
D. Arne
Keyword(s):  
British Columbia ◽  
Limit Of Detection ◽  
Principal Component ◽  
Mineral Deposit ◽  
Stream Sediment ◽  
Training Dataset ◽  
Geochemical Data ◽  
Posterior Probabilities ◽  
Multivariate Statistical ◽  
Log Ratio

In this study we apply multivariate statistical and predictive classification methods to interpret geochemical data from 8545 stream-sediment samples collected in southern British Columbia, Canada. Data for 35 elements were corrected for laboratory bias and adjusted for values reported below the lower limit of detection. Each sample site was attributed with the closest British Columbia MINFILE occurrence within 2.5 km. MINFILE occurrences were grouped into ‘GroupModels’ based on similarities between the British Columbia Geological Survey mineral deposit models and geochemical signatures. These data were used to create a training dataset of 474 observations, including 100 samples not attributed with a MINFILE occurrence. The training set was used to generate predictions for the mineral deposit models from which posterior probabilities were estimated for the remaining 8071 samples. The data underwent a centred log-ratio transformation and then characterization using either principal component analysis (PCA) or t-distributed stochastic neighbour embedding using 9 dimensions (t-SNE) prior to classification by random forests. The posterior probabilities generated from the t-SNE metric provide a slightly higher level of prediction accuracy compared to the posterior probabilities obtained using the PCA metric. The results are comparable to those obtained using a conventional catchment analysis approach and expert-driven model. The approach presented here provides a repeatable, consistent and defensible methodology for the identification of prospective mineralized terrains and mineral systems.

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