Surficial geologic units are unconsolidated materials overlying bedrock. The dataset represents a general description of the stratigraphy and geology, including geologic unit thickness, morphology, age and rank. It features a list of the geologic unit names and types of sediment in the hydrogeological unit. While the preferred format to deliver this data is by using a shapefile and its linked attributes, this dataset can be delivered also by providing link to external data which should have at least the same properties or also by joining a georeferenced image of the map.

The confinement describes the types of aquifer: confined, unconfined and semi-confined. Confined aquifer is bounded from above and below by impervious formations. Unconfined aquifer has a water table which serves as its upper boundary. Semi-confined aquifer is in between. Aquifer confinement is derived from geology, stratigraphy and hydrogeological unit thickness. The dataset represents the confinement assessment of the local area over the hydrogeological unit, from a controlled vocabulary.

The bedrock geologic units designate units of the same types of rock which composed the solid rock exposed at ground surface (as outcrop) or which underlies unconsolidated surficial sediments. This dataset represents a general description of the stratigraphy and geology, including geologic unit thickness, morphology, age and rank. It features a list of the geologic unit names and types of rock (lithology) in the hydrogeological unit, from a controlled vocabulary. While the preferred format to deliver this data is by using a shapefile and its linked attributes, this dataset can be delivered also by providing link to external data which should have at least the same properties or also by joining a georeferenced image of the map.

The Hydrogeological Information System (HIS) contains the physical characteristics of wells and boreholes (location, depth, stratigraphy encountered, etc.) mainly from drilling reports transmitted by well drillers for groundwater sampling installations serving private drinking water residences. The geographic coverage corresponds to all of Quebec although most of the data are found in the south of the province. A monthly update is made.SIH data can be useful for hydrogeology professionals, the academic community and a wider audience in order to make interpretations on portions of territory, for example on the depth of the rock in a sector, or to consult more precise points of information such as the description of a specific identified well.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

The lithogeochemical data provides the chemical composition of rock samples. This data helps characterize the rocks and can be used to understand their mode of formation.Distributed from [GeoYukon](https://yukon.ca/geoyukon) by the [Government of Yukon](https://yukon.ca/maps) . Discover more digital map data and interactive maps from Yukon's digital map data collection.For more information: [geomatics.help@yukon.ca](mailto:geomatics.help@yukon.ca)

Prospectivity model highlights areas of Canada with the greatest potential for magmatic nickel deposits. The preferred prospectivity model is based on public geological, geochemical, and geophysical datasets that were spatially indexed using the H3 discrete global grid system. Each H3 cell is associated with a prospectivity value, or class probability, calculated from the best-performing gradient boosting machines model. Model results are filtered to include the top 20% of prospectivity values for visualization purposes.

Prospectivity model highlights areas of Canada with the greatest potential for clastic-dominated zinc deposits. The preferred prospectivity model is based on public geological, geochemical, and geophysical datasets that were spatially indexed using the H3 discrete global grid system. Each H3 cell is associated with a prospectivity value, or class probability, calculated from the best-performing gradient boosting machines model. Model results are filtered to include the top 20% of prospectivity values for visualization purposes.

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.

Prospectivity model highlights areas of Canada with the greatest potential for Mississippi Valley-type zinc deposits. The preferred prospectivity model is based on public geological, geochemical, and geophysical datasets that were spatially indexed using the H3 discrete global grid system. Each H3 cell is associated with a prospectivity value, or class probability, calculated from the best-performing gradient boosting machines model. Model results are filtered to include the top 20% of prospectivity values for visualization purposes.