Grants and Contributions:

Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023)

Geostatistics uses statistical techniques to assess resources/reserves for geological deposits. These techniques have become increasingly popular for understanding uncertainty in sparsely sampled mineral deposits, petroleum reservoirs and other spatially distributed phenomenon. This work relates to the development of techniques to better model deposits that exhibit complex geological features; these features are modeled using rock types that define a particular rock type (such as sand stone, shale, granite, etc). Usually a geologist interprets drill hole data/images to determine rock types but numerical modeling requires statistical assumptions about each rock type that may not be correct depending on the geological definition.

The first aspect of the planned research is to explore the modeling implications of the definition of rock types from a statistical point of view. Each sample taken from an ore deposit will be assigned a rock type based on the quantitative data available (such as mineral grade, geophysical surveys, contaminate levels) and the qualitative data available (such as the geological interpretation). This will ensure that rock types meet all necessary modeling assumptions but will also maintain the benefit of geological knowledge from qualitative data.

The second aspect of this research is to improve large scale ore body limits modeling. This involves interpreting the available data for a deposit and determining the mineralization extents. Normally this is done using the geological knowledge of the deposit; however, an automated method is proposed as a starting point for geological interpretation to improve models of mineralization.
This research is generally directed towards all disciplines where spatial modeling is required, including but not limited to: mineral resource/reserve modeling; mine planning; contaminate modeling; petroleum resource modeling. However, the research will be demonstrated on mineral deposits. The anticipated outcomes of this work are methodologies, computational programs and modeling recommendations for the assignment of categories (i.e. rock types, facies, etc) to sample data as well as automatic large scale mineralization extents modeling.

Engineering decisions are made based on these numerical models, including: mine plans; environmental footprints of mines; stockpiling decisions; plant processing input feeds. The benefits of the proposed work are to account for known uncertainties and increase the accuracy of numerical models, resulting in improved engineering decision making. The benefits of this research to Canada will be the increased competitive advantage for Canadian mining companies due to better modeling of ore bodies. More accurate models of ore deposits will be constructed, resulting in better mine plans with increased profits and sustainability of mining in Canada.