GIS compilation of data used to perform the stacked cumulative chance of success (resource potential map) in Open file 9163. Natural Resources Canada (NRCan) has been tasked, under the Marine Conservation Targets (MCT) initiative announced in Budget 2016, with evaluating the petroleum resource potential for areas identified for possible protection as part of the Government of Canada's commitment to conserve 10% of its marine areas by 2020. As part of this initiative, NRCan's Geological Survey of Canada (GSC) conducted a broad regional study of the petroleum potential over the majority of the Magdalen Basin, which is the principal geological basin in the southern Gulf of St. Lawrence. The GSC resource assessment is visually represented by a qualitative petroleum potential map. Disclaimer: A simplified colored version of the map is displayed on the Web Mapping Service (WMS). The correct version is available for download through the Federal Geospatial Platform (FGP) and GEOSCAN.

GIS compilation of data used to perform the stacked cumulative chance of success (resource potential map) in Open file 8556. Natural Resources Canada (NRCan) has been tasked, under the Marine Conservation Targets (MCT) initiative announced in Budget 2016, with evaluating the petroleum resource potential for areas identified for possible protection as part of the Government of Canada's commitment to conserve 10% of its marine areas by 2020. As part of this initiative, NRCan's Geological Survey of Canada (GSC) conducted a broad regional study of the petroleum potential over the majority of the Magdalen Basin, which is the principal geological basin in the southern Gulf of St. Lawrence. The GSC resource assessment is visually represented by a qualitative petroleum potential map. Disclaimer: A simplified colored version of the map is displayed on the Web Mapping Service (WMS). The correct version is available for download through the Federal Geospatial Platform (FGP) and GEOSCAN.

A revised qualitative assessment of the hydrocarbon resource potential is presented for the Hudson Bay sedimentary basin that underlies Hudson Bay and adjacent onshore areas of Ontario, Manitoba, and Nunavut. The Hudson Basin is a large intracratonic sedimentary basin thatpreserves dominantly Ordovician to Devonian aged limestone and evaporite strata. Maximum preserved sediment thickness is about 2.5 km. Source rock is the petroleum system element that has the lowest chance of success; the potential source rock is thin, may be discontinuous, and the thin sedimentarycover may not have been sufficient to achieve the temperatures required to generate and expel oil from a source rock over much of the basin. The highest potential is in the center of the basin, where the hydrocarbon potential is considered amp;lt;'Mediumamp;gt;'. Hydrocarbon potential decreasestowards the edges of the basin due to fewer plays being present, and thinner strata reduce the chance of oil generation and expulsion. Quantitative hydrocarbon assessment considers seven plays. Input parameters for field size and field density (per unit area) are based on analog Michigan, Williston,and Illinois intracratonic sedimentary basins that are about the same age and that had similar depositional settings to Hudson Basin. Basin-wide play and local prospect chances of success were assigned based on local geological conditions in Hudson Bay. Each of the seven plays were analyzed in Roseand Associates PlayRA software, which performs a Monte Carlo simulation using the local chance of success matrix and field size and prospect numbers estimated from analog basins. Hudson sedimentary basin has a mean estimate of 67.3 million recoverable barrels of oil equivalent and a 10% chance ofhaving 202.2 or more million barrels of recoverable oil equivalent. The mean chance for the largest expected pool is about 15 million recoverable barrels of oil equivalent (MMBOE), and there is only a 10% chance of there being a field larger than 23.2 MMBOE recoverable. The small expected fieldsizes are based on the large analog data set from Michigan, Williston and Illinois basins, and are due to the geological conditions that create the traps. The small size of the largest expected field, the low chance of exploration success, and the small overall resource make it unlikely that there are any economically recoverable hydrocarbons in the Hudson Basin in the foreseeable future. The Southampton Island area of interest includes 93 087 km2 of nearshore waters around Southampton Island and Chesterfield Inlet in the Kivalliq Region of Nunavut. Of the total resource estimated for Hudson Bay, 14 million barrels are apportioned to the Southampton Island Area of Interest.

This model is derived from geological, geophysical and other forms of geodata. Feature extraction used deep learning. Predictive modelling made use of the deep ensemble method. Displayed is a Pan-Canadian probability map of mineral potential of graphite. This map was generated using known graphite deposits and occurrences and their associated features. Higher probability values highlight areas with an increased probability of graphite mineral systems.

This model is derived from geological and geophysical data, which is processed using deep learning and natural language processing techniques. Displayed is a Pan-Canadian probability map indicating the likelihood of discovering next-generation lithium-cesium-tantalum (LCT) pegmatites. This map was generated using known Canadian LCT pegmatites and their associated geospatial features, incorporating geological and geophysical data analyzed through deep learning and natural language processing techniques. Higher probability values highlight areas with an increased likelihood of hosting next-generation deposits, making this map a valuable tool for decision-making.

A predictive model for Canadian carbonatite-hosted REE ± Nb deposits is presented herein. This model was developed by integrating diverse data layers derived from geophysical, geochronological, and geological sources. These layers represent the key components of carbonatite-hosted REE ± Nb mineral systems, including the source, transport mechanisms, geological traps, and preservation processes. Deep learning algorithms were employed to integrate these layers into a comprehensive predictive framework. Here is a link to the publication that describes this product: https://link.springer.com/article/10.1007/s11053-024-10369-7

The Canada Geological Map Compilation (CGMC) is a database of previously published bedrock geological maps sourced from provincial, territorial, and other geological survey organizations. The geoscientific information included within these source geological maps wasstandardized, translated to English, and combined to provide complete coverage of Canada and support a range of down-stream machine learning applications. Detailed lithological, mineralogical, metamorphic, lithostratigraphic, and lithodemic information was not previously available as onenational-scale product. The source map data was also enhanced by correcting geometry errors and through the application of a new hierarchical generalized lithology classification scheme to subdivide the original rocks types into 35 classes. Each generalized lithology is associated with asemi-quantitative measure of classification uncertainty. Lithostratigraphic and lithodemic names included within the source maps were matched with the Lexicon of Canadian Geological Names (Weblex) wherever possible and natural language processing was used to transform all of the available text-basedinformation into word tokens. Overlapping map polygons and boundary artifacts across political boundaries were not addressed as part of this study. As a result, the CGMC is a patchwork of overlapping bedrock geological maps with varying scale (1:30,000-1:5,000,000), publication year (1996-2023), andreliability. Preferred geological and geochronological maps of Canada are presented as geospatial rasters based on the best available geoscientific information extracted from these overlapping polygons for each map pixel. New higher resolution geological maps will be added over time to fill datagaps and to update geoscientific information for future applications of the CGMC.