This compilation contains a list of approximately 8,600 sites across the United States, Canada, and Australia where Zn-Pb-mineralized rock is attributed to basinal brine-related mineralizing processes, specifically assigned to Mississippi Valley Type (MVT) or clastic-dominated (CD) deposit types; a second group of 147 sites, classified as “unknown”, but which may have similar genesis, is also included. These sites were selected based on interpretations of 16 published databases, including the Mineral Resources Data System (USGS, 2016) and the Alaska Resource Data File (USGS, 1996) for the United States, and comprise a significant but not necessarily complete dataset. Each site is further classified by deposit type and development status. For the limited deposits where grade and tonnage information are available, tonnage and published Cu, Zn, Pb, Ag, and Au grades are provided. References for source data are also included. References U.S. Geological Survey, 1996, Alaska Resource Data File, (ver 1.7, March, 2018): U.S. Geological Survey data release, 2605 p., https://doi.org/10.5066/P96MMRFD. U.S. Geological Survey, 2016, Mineral Resources Data System: U.S. Geological Survey, Reston, Virginia, at https://mrdata.usgs.gov/mrds/.

This compilation contains a list of approximately 8,600 sites across the United States, Canada, and Australia where Zn-Pb-mineralized rock is attributed to basinal brine-related mineralizing processes, specifically assigned to Mississippi Valley Type (MVT) or clastic-dominated (CD) deposit types; a second group of 147 sites, classified as “unknown”, but which may have similar genesis, is also included. These sites were selected based on interpretations of 16 published databases, including the Mineral Resources Data System (USGS, 2016) and the Alaska Resource Data File (USGS, 1996) for the United States, and comprise a significant but not necessarily complete dataset. Each site is further classified by deposit type and development status. For the limited deposits where grade and tonnage information are available, tonnage and published Cu, Zn, Pb, Ag, and Au grades are provided. References for source data are also included. References U.S. Geological Survey, 1996, Alaska Resource Data File, (ver 1.7, March, 2018): U.S. Geological Survey data release, 2605 p., https://doi.org/10.5066/P96MMRFD. U.S. Geological Survey, 2016, Mineral Resources Data System: U.S. Geological Survey, Reston, Virginia, at https://mrdata.usgs.gov/mrds/.

GeoTiff grids of models of prospectivity for clastic-dominated (CD) and Mississippi Valley-type (MVT) Pb-Zn mineralization for the US and Canada (combined) and Australia that used data provided in this report are provided here. The models are the result of a study by Lawley and others (2022) that used a data-driven machine learning approach called Gradient Boosting to predict the mineral prospectivity for clastic-dominated (CD) and carbonate-hosted (MVT) deposits across the United States, Canada, and Australia. The study was part of a tri-national collaboration between the U.S. Geological Survey, the Canadian Geological Survey, and Geoscience Australia called the Critical Minerals Mapping Initiative. The original models were calculated using the H2O artificial intelligence platform and output as H3 Discrete Global Grids developed by Uber (Uber Technologies Inc., 2020). The Uber grids are based on a hexagonal geometry with an average area of 5.16 km2. The Uber grids were converted to GeoTiff raster grids that approximate a 2 km by 2 km grid for this report. The full description on how the models were produced are described in Lawley and others (2021, 2022). References Lawley, C.J.M., McCafferty, A.E., Graham, G.E., Gadd, M.G., Huston, D.L., Kelley, K.D., Paradis, S., Peter, J.M., and Czarnota, K., 2021, Datasets to support prospectivity modelling for sediment-hosted Zn-Pb mineral systems: Natural Resources Canada Open File 8836, https://doi.org/10.4095/329203. Lawley, C.J.M., McCafferty, A.E., Graham, G.E., Huston, D.L., Kelley, K.D., Czarnota, K., Paradis, S., Peter, J.M., Hayward, N., Barlow, M., Emsbo, P., Coyan, J., San Juan, C.A., and Gadd, M.G., 2022, Data-driven prospectivity modelling of sediment-hosted Zn-Pb mineral systems and their critical raw materials: Ore Geology Reviews, v. 141, no. 104635, https://doi.org/10.1016/j.oregeorev.2021.104635. Uber Technologies Inc., 2020, H3: A hexagonal hierarchical geospatial indexing system: GitHub repository, accessed July 1, 2021, at https://github.com/uber/h3.

GeoTiff grids of models of prospectivity for clastic-dominated (CD) and Mississippi Valley-type (MVT) Pb-Zn mineralization for the US and Canada (combined) and Australia that used data provided in this report are provided here. The models are the result of a study by Lawley and others (2022) that used a data-driven machine learning approach called Gradient Boosting to predict the mineral prospectivity for clastic-dominated (CD) and carbonate-hosted (MVT) deposits across the United States, Canada, and Australia. The study was part of a tri-national collaboration between the U.S. Geological Survey, the Canadian Geological Survey, and Geoscience Australia called the Critical Minerals Mapping Initiative. The original models were calculated using the H2O artificial intelligence platform and output as H3 Discrete Global Grids developed by Uber (Uber Technologies Inc., 2020). The Uber grids are based on a hexagonal geometry with an average area of 5.16 km2. The Uber grids were converted to GeoTiff raster grids that approximate a 2 km by 2 km grid for this report. The full description on how the models were produced are described in Lawley and others (2021, 2022). References Lawley, C.J.M., McCafferty, A.E., Graham, G.E., Gadd, M.G., Huston, D.L., Kelley, K.D., Paradis, S., Peter, J.M., and Czarnota, K., 2021, Datasets to support prospectivity modelling for sediment-hosted Zn-Pb mineral systems: Natural Resources Canada Open File 8836, https://doi.org/10.4095/329203. Lawley, C.J.M., McCafferty, A.E., Graham, G.E., Huston, D.L., Kelley, K.D., Czarnota, K., Paradis, S., Peter, J.M., Hayward, N., Barlow, M., Emsbo, P., Coyan, J., San Juan, C.A., and Gadd, M.G., 2022, Data-driven prospectivity modelling of sediment-hosted Zn-Pb mineral systems and their critical raw materials: Ore Geology Reviews, v. 141, no. 104635, https://doi.org/10.1016/j.oregeorev.2021.104635. Uber Technologies Inc., 2020, H3: A hexagonal hierarchical geospatial indexing system: GitHub repository, accessed July 1, 2021, at https://github.com/uber/h3.

GeoTiff grids of models of prospectivity for clastic-dominated (CD) and Mississippi Valley-type (MVT) Pb-Zn mineralization for the US and Canada (combined) and Australia that used data provided in this report are provided here. The models are the result of a study by Lawley and others (2022) that used a data-driven machine learning approach called Gradient Boosting to predict the mineral prospectivity for clastic-dominated (CD) and carbonate-hosted (MVT) deposits across the United States, Canada, and Australia. The study was part of a tri-national collaboration between the U.S. Geological Survey, the Canadian Geological Survey, and Geoscience Australia called the Critical Minerals Mapping Initiative. The original models were calculated using the H2O artificial intelligence platform and output as H3 Discrete Global Grids developed by Uber (Uber Technologies Inc., 2020). The Uber grids are based on a hexagonal geometry with an average area of 5.16 km2. The Uber grids were converted to GeoTiff raster grids that approximate a 2 km by 2 km grid for this report. The full description on how the models were produced are described in Lawley and others (2021, 2022). References Lawley, C.J.M., McCafferty, A.E., Graham, G.E., Gadd, M.G., Huston, D.L., Kelley, K.D., Paradis, S., Peter, J.M., and Czarnota, K., 2021, Datasets to support prospectivity modelling for sediment-hosted Zn-Pb mineral systems: Natural Resources Canada Open File 8836, https://doi.org/10.4095/329203. Lawley, C.J.M., McCafferty, A.E., Graham, G.E., Huston, D.L., Kelley, K.D., Czarnota, K., Paradis, S., Peter, J.M., Hayward, N., Barlow, M., Emsbo, P., Coyan, J., San Juan, C.A., and Gadd, M.G., 2022, Data-driven prospectivity modelling of sediment-hosted Zn-Pb mineral systems and their critical raw materials: Ore Geology Reviews, v. 141, no. 104635, https://doi.org/10.1016/j.oregeorev.2021.104635. Uber Technologies Inc., 2020, H3: A hexagonal hierarchical geospatial indexing system: GitHub repository, accessed July 1, 2021, at https://github.com/uber/h3.

These data present geologic map units for the United States (Horton and others, 2017; Wilson and others, 2015) and Australia (Raymond and others, 2012) reclassified to 31 generalized sub-type lithologic groups of igneous, metamorphic, and sedimentary rocks (Lawley and others, 2022). These generalized classifications are based on interpretation of map unit descriptions in the different map compilations. Given that map unit descriptions often contain multiple rock types, there were subjective calls necessary when assigning generalized lithologic classification. The data were developed as part of the tri-national Critical Minerals Mapping Initiative (Kelley, 2020) between the United States, Canada, and Australia, an effort to model and map prospectivity for basin-hosted Pb-Zn mineralization. A national-scale geologic map compilation for Canada is not publicly available. Therefore, Lawley and others (2021) compiled geologic source maps to produce a gridded model layer that is provided in this data release in the Child Items section “Gridded geology shapefiles for the United States, Canada, and Australia.” References Horton, J.D., San Juan, C.A., and Stoeser, D.B., 2017, The State Geologic Map Compilation (SGMC) geodatabase of the conterminous United States (ver. 1.1, August 2017): U.S. Geological Survey Data Series 1052, 46 p., https://doi.org/10.3133/ds1052. Kelley, K.D., 2020, International geoscience collaboration to support critical mineral discovery: U.S. Geological Survey Fact Sheet 2020-3035, 2 p., https://doi.org/10.3133/fs20203035. Lawley, C.J.M., McCafferty, A.E., Graham, G.E., Gadd, M.G., Huston, D.L., Kelley, K.D., Paradis, S., Peter, J.M., and Czarnota, K., 2021, Datasets to support prospectivity modelling for sediment-hosted Zn-Pb mineral systems: Natural Resources Canada Open File 8836, https://doi.org/10.4095/329203. Lawley, C.J.M., McCafferty, A.E., Graham, G.E., Huston, D.L., Kelley, K.D., Czarnota, K., Paradis, S., Peter, J.M., Hayward, N., Barlow, M., Emsbo, P., Coyan, J., San Juan, C.A., and Gadd, M.G., 2022, Data-driven prospectivity modelling of sediment-hosted Zn-Pb mineral systems and their critical raw materials: Ore Geology Reviews, v. 141, no. 104635, https://doi.org/10.1016/j.oregeorev.2021.104635. Raymond, O.L., Liu, S., Gallagher, R., Zhang, W., and Highet, L.M., 2012, Surface Geology of Australia 1:1 million scale dataset 2012 edition: Geoscience Australia, http://pid.geoscience.gov.au/dataset/ga/74619. Wilson, F.H., Hults, C.P., Mull, C.G., and Karl, S.M., comps., 2015, Geologic map of Alaska: U.S. Geological Survey Scientific Investigations Map 3340, 2 sheets, scale 1:1,584,000, 196-p. pamphlet, https://doi.org/10.3133/sim3340.

These data present geologic map units for the United States (Horton and others, 2017; Wilson and others, 2015) and Australia (Raymond and others, 2012) reclassified to 31 generalized sub-type lithologic groups of igneous, metamorphic, and sedimentary rocks (Lawley and others, 2022). These generalized classifications are based on interpretation of map unit descriptions in the different map compilations. Given that map unit descriptions often contain multiple rock types, there were subjective calls necessary when assigning generalized lithologic classification. The data were developed as part of the tri-national Critical Minerals Mapping Initiative (Kelley, 2020) between the United States, Canada, and Australia, an effort to model and map prospectivity for basin-hosted Pb-Zn mineralization. A national-scale geologic map compilation for Canada is not publicly available. Therefore, Lawley and others (2021) compiled geologic source maps to produce a gridded model layer that is provided in this data release in the Child Items section “Gridded geology shapefiles for the United States, Canada, and Australia.” References Horton, J.D., San Juan, C.A., and Stoeser, D.B., 2017, The State Geologic Map Compilation (SGMC) geodatabase of the conterminous United States (ver. 1.1, August 2017): U.S. Geological Survey Data Series 1052, 46 p., https://doi.org/10.3133/ds1052. Kelley, K.D., 2020, International geoscience collaboration to support critical mineral discovery: U.S. Geological Survey Fact Sheet 2020-3035, 2 p., https://doi.org/10.3133/fs20203035. Lawley, C.J.M., McCafferty, A.E., Graham, G.E., Gadd, M.G., Huston, D.L., Kelley, K.D., Paradis, S., Peter, J.M., and Czarnota, K., 2021, Datasets to support prospectivity modelling for sediment-hosted Zn-Pb mineral systems: Natural Resources Canada Open File 8836, https://doi.org/10.4095/329203. Lawley, C.J.M., McCafferty, A.E., Graham, G.E., Huston, D.L., Kelley, K.D., Czarnota, K., Paradis, S., Peter, J.M., Hayward, N., Barlow, M., Emsbo, P., Coyan, J., San Juan, C.A., and Gadd, M.G., 2022, Data-driven prospectivity modelling of sediment-hosted Zn-Pb mineral systems and their critical raw materials: Ore Geology Reviews, v. 141, no. 104635, https://doi.org/10.1016/j.oregeorev.2021.104635. Raymond, O.L., Liu, S., Gallagher, R., Zhang, W., and Highet, L.M., 2012, Surface Geology of Australia 1:1 million scale dataset 2012 edition: Geoscience Australia, http://pid.geoscience.gov.au/dataset/ga/74619. Wilson, F.H., Hults, C.P., Mull, C.G., and Karl, S.M., comps., 2015, Geologic map of Alaska: U.S. Geological Survey Scientific Investigations Map 3340, 2 sheets, scale 1:1,584,000, 196-p. pamphlet, https://doi.org/10.3133/sim3340.