National-scale geologic, geophysical, and mineral resource raster and vector data covering the United States, Canada, and Australia are provided in this data release. The data were compiled as part of the tri-national Critical Minerals Mapping Initiative (CMMI). The CMMI, established in 2019, is an international science collaboration between the U.S. Geological Survey (USGS), Geoscience Australia (GA), and the Geological Survey of Canada (GSC). One aspect of the CMMI is to use national- to global-scale earth science data to map where critical mineral prospectivity may exist using advanced machine learning approaches (Kelley, 2020). The geoscience information presented in this report include the training and evidential layers that cover all three countries and underpin the resultant prospectivity models for basin-hosted Pb-Zn mineralization described in Lawley and others (2021). It is expected that these data layers will be useful to many regional- to continental-scale studies related to a wide range of earth science research. Therefore, the data layers are organized using widely accepted GIS formats in the same map projection to increase efficiency and effectiveness of future studies. All datasets have a common geographic projection in decimal degrees using a WGS84 datum. Data for the various training and evidential layers were either derived for this study or were extracted from previous national to global-scale compilations. Data from outside work are provided here as a courtesy for completeness of the model and should be cited as the original source. Original references are provided on each child page. Where possible, data for the United States were merged to data for Canada to provide composite data that allow for continuity and seamless analyses of the earth science data across the two countries. Earth science data provided in this report include training data for the models. Training data include a mineral resource database of Pb-Zn deposits and occurrences related to either carbonate-hosted (Mississippi Valley type-MVT) or clastic-dominated (aka sedex) Pb-Zn mineralization. Evidential layers that were used as input to the models include GeoTIFF grid files consisting of ground, airborne, and satellite geophysical data (magnetic, gravity, tomography, seismic) and several related derivative products. Geologic layers incorporated into the models include shapefiles of modified lithology and faults for the United States, Canada and Australia. A global database of ancient and modern passive margins is provided here as well as a link to a database mapping the global distribution of black shale units from a previous USGS study. GeoTIFF grids of the final prospectivity models for MVT and for clastic-dominated Pb-Zn mineralization across the US, Canada, and Australia from Lawley and others (2021) are also included. Each child page describes the particular data layer and related derivative products if applicable. 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., 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.

Mineral Land Classification studies are produced by the State Geologist as specified by the Surface Mining and Reclamation Act (SMARA, PRC 2710 et seq.) of 1975. To address mineral resource conservation, SMARA mandated a two-phase process called classification-designation. Classification is carried out by the State Geologist and designation is a function of the State Mining and Geology Board. The classification studies contained here evaluate the mineral resources and present this information in the form of Mineral Resource Zones. The objective of the classification-designation process is to ensure, through appropriate local lead agency policies and procedures, that mineral materials will be available when needed and do not become inaccessible as a result of inadequate information during the land-use decision-making process.

Mineral resource occurrence data covering the world, most thoroughly within the U.S. This database contains the records previously provided in the Mineral Resource Data System (MRDS) of USGS and the Mineral Availability System/Mineral Industry Locator System (MAS/MILS) originated in the U.S. Bureau of Mines, which is now part of USGS. The MRDS is a large and complex relational database developed over several decades by hundreds of researchers and reporters. While database records describe mineral resources worldwide, the compilation of information was intended to cover the United States completely, and its coverage of resources in other countries is incomplete. The content of MRDS records was drawn from reports previously published or made available to USGS researchers. Some of those original source materials are no longer available. The information contained in MRDS was intended to reflect the reports used as sources and is current only as of the date of those source reports. Consequently MRDS does not reflect up-to-date changes to the operating status of mines, ownership, land status, production figures and estimates of reserves and resources, or the nature, size, and extent of workings. Information on the geological characteristics of the mineral resource are likely to remain correct, but aspects involving human activity are likely to be out of date.

Spatial coverage index compiled by East View Geospatial of set "Morocco 1:1,000,000 Scale Geological and Mineral Resource Maps". Source data from SGM (publisher). Type: Geoscientific - Geology. Scale: 1:1,000,000. Region: Africa, Middle East.

Version 10.0 (Alaska, Hawaii and Puerto Rico added) of these data are part of a larger U.S. Geological Survey (USGS) project to develop an updated geospatial database of mines, mineral deposits, and mineral regions in the United States. Mine and prospect-related symbols, such as those used to represent prospect pits, mines, adits, dumps, tailings, etc., hereafter referred to as “mine” symbols or features, have been digitized from the 7.5-minute (1:24,000, 1:25,000-scale; and 1:10,000, 1:20,000 and 1:30,000-scale in Puerto Rico only) and the 15-minute (1:48,000 and 1:62,500-scale; 1:63,360-scale in Alaska only) archive of the USGS Historical Topographic Map Collection (HTMC), or acquired from available databases (California and Nevada, 1:24,000-scale only). Compilation of these features is the first phase in capturing accurate locations and general information about features related to mineral resource exploration and extraction across the U.S. The compilation of 725,690 point and polygon mine symbols from approximately 106,350 maps across 50 states, the Commonwealth of Puerto Rico (PR) and the District of Columbia (DC) has been completed: Alabama (AL), Alaska (AK), Arizona (AZ), Arkansas (AR), California (CA), Colorado (CO), Connecticut (CT), Delaware (DE), Florida (FL), Georgia (GA), Hawaii (HI), Idaho (ID), Illinois (IL), Indiana (IN), Iowa (IA), Kansas (KS), Kentucky (KY), Louisiana (LA), Maine (ME), Maryland (MD), Massachusetts (MA), Michigan (MI), Minnesota (MN), Mississippi (MS), Missouri (MO), Montana (MT), Nebraska (NE), Nevada (NV), New Hampshire (NH), New Jersey (NJ), New Mexico (NM), New York (NY), North Carolina (NC), North Dakota (ND), Ohio (OH), Oklahoma (OK), Oregon (OR), Pennsylvania (PA), Rhode Island (RI), South Carolina (SC), South Dakota (SD), Tennessee (TN), Texas (TX), Utah (UT), Vermont (VT), Virginia (VA), Washington (WA), West Virginia (WV), Wisconsin (WI), and Wyoming (WY). The process renders not only a more complete picture of exploration and mining in the U.S., but an approximate timeline of when these activities occurred. These data may be used for land use planning, assessing abandoned mine lands and mine-related environmental impacts, assessing the value of mineral resources from Federal, State and private lands, and mapping mineralized areas and systems for input into the land management process. These data are presented as three groups of layers based on the scale of the source maps. No reconciliation between the data groups was done.Datasets were developed by the U.S. Geological Survey Geology, Geophysics, and Geochemistry Science Center (GGGSC). Compilation work was completed by USGS National Association of Geoscience Teachers (NAGT) interns: Emma L. Boardman-Larson, Grayce M. Gibbs, William R. Gnesda, Montana E. Hauke, Jacob D. Melendez, Amanda L. Ringer, and Alex J. Schwarz; USGS student contractors: Margaret B. Hammond, Germán Schmeda, Patrick C. Scott, Tyler Reyes, Morgan Mullins, Thomas Carroll, Margaret Brantley, and Logan Barrett; and by USGS personnel Virgil S. Alfred, Damon Bickerstaff, E.G. Boyce, Madelyn E. Eysel, Stuart A. Giles, Autumn L. Helfrich, Alan A. Hurlbert, Cheryl L. Novakovich, Sophia J. Pinter, and Andrew F. Smith.USMIN project website: https://www.usgs.gov/USMIN

Multispectral remote sensing data acquired by Landsat 8 Operational Land Imager (OLI) sensor were analyzed using an automated technique to generate surficial mineralogy and vegetation maps of the conterminous western United States. Six spectral indices (e.g. band-ratios), highlighting distinct spectral absorptions, were developed to aid in the identification of mineral groups in exposed rocks, soils, mine waste rock, and mill tailings across the landscape. The data are centered on the Western U.S. and cover portions of Texas, Oklahoma, Kansas, the Canada-U.S. border, and the Mexico-U.S. border during the summers of 2013 – 2014. Methods used to process the images and algorithms used to infer mineralogical composition of surficial materials are detailed in Rockwell and others (2021) and were similar to those developed by Rockwell (2012; 2013). Final maps are provided as ERDAS IMAGINE (.img) thematic raster images and contain pixel values representing mineral and vegetation group classifications. Rockwell, B.W., 2012, Description and validation of an automated methodology for mapping mineralogy, vegetation, and hydrothermal alteration type from ASTER satellite imagery with examples from the San Juan Mountains, Colorado: U.S. Geological Survey Scientific Investigations Map 3190, 35 p. pamphlet, 5 map sheets, scale 1:100,000, http://doi.org/10.13140/RG.2.1.2769.9365. Rockwell, B.W., 2013, Automated mapping of mineral groups and green vegetation from Landsat Thematic Mapper imagery with an example from the San Juan Mountains, Colorado: U.S. Geological Survey Scientific Investigations Map 3252, 25 p. pamphlet, 1 map sheet, scale 1:325,000, http://doi.org/10.13140/RG.2.1.2507.7925. Rockwell, B.W., Gnesda, W.R., and Hofstra, A.H., 2021, Improved automated identification and mapping of iron sulfate minerals, other mineral groups, and vegetation from Landsat 8 Operational Land Imager Data: San Juan Mountains, Colorado, and Four Corners Region: U.S. Geological Survey Scientific Investigations Map 3466, scale 1:325,000, 51 p. pamphlet, https://doi.org/10.3133/sim3466/.

Spatial coverage index compiled by East View Geospatial of set "Angola 1:1,000,000 Scale Mineral Resources Maps (4 sheets)". Source data from INGA (publisher). Type: Geoscientific - Geology. Scale: 1:1,000,000. Region: Africa.

Abstract This layer contains point locations of major mineral deposits, and includes the geological setting, the timing and type of mineralisation, global resource endowments, associated host and igneous rocks, alteration assemblages, and metamorphism, where known. The deposits were selected as they have substantial endowment (i.e. pre-mining mineral resource) and/or detailed geological information is available. For each deposit (or, in some cases, district) the dataset includes information on: 1. Name (including synonyms), location and GA identifying numbers; 2. Tectonic province that hosts the deposit; 3. Type(s) and age(s) of mineralising events that produced/affected the deposit (including metadata on ages); 4. The metal/mineral endowment of the deposit; 5. Host rocks to the deposit; 6. Spatially and/or temporally associated magmatic rocks; 7. Spatially and temporally associated alteration assemblages (mostly proximal, but, in some cases, regional assemblages); 8. The Fe-S-O minerals present in the deposit and relative abundances where known; 9. Sulfate minerals present; 10. Peak metamorphic grade; 11. Data sources; and 12. Comments. For many commodities, there are many hundreds or thousands of deposits and occurrences around Australia, with only a small fraction of these deposits/occurrences making a significant contribution to Australia’s mineral endowment. This dataset contains information about these deposits. In some cases, a number of small deposits have been grouped together into a district, but in other cases, small deposits have been ignored. However, where important information, such as the age of small deposits or occurrences are available, they have been included. This document presents more detailed descriptions of the metadata presented in the compilation. The dataset is presented in Appendix A. Appendix B presents a national classification of geological provinces based mostly on existing State survey classifications; Appendix C presents a deposit classification based on the classification proposed by Hofstra et al. (2021); and Appendix D presents mineral abbreviations used in the dataset. The Annexes are available here: Geological setting, age and endowment of major Australian mineral deposits - a compilation. Data Dictionary Australian Minerals Data - Mineral Deposits - Geological Setting, Age and Endowment

Attribute Name Description

DEPOSIT_ENO Deposit (ENO)

DEPOSIT_PID Deposit Persistent ID (PID)

DEPOSIT_NAME Preferred name of the mineral occurrence, prospect, or deposit as recorded on a map or other source reference.

SYNONYMS Alternative names that may have been given to the occurrence/deposit

LONGITUDE_GDA94 Longitude in GDA94

LATITUDE_GDA94 Latitude in GDA94

STATE State in Australia

COMMODITIES The earth resource commodity (eg Cu, Au, Fe)

OPERATING_STATUS Describes the current stage of development of the deposit, prospect, or mineral occurrence

MINING_DISTRICT Mining District

SUPERPROVINCE_ENO Superprovince ENO

SUPERPROVINCE_NAME Superprovince name

PROVINCE_ENO Province ENO

PROVINCE_NAME Province name

SUBPROVINCE_ENO Subprovince ENO

SUBPROVINCE_NAME Subprovince name

DEPOSIT_ENVIRONMENT Deposit environment

DEPOSIT_GROUP Deposit group

DEPOSIT_TYPE Deposit type

FIRST_EVENTNO Event number

FIRST_EVENT_NAME Event name

FIRST_EVENT_TIMING Event timing

FIRST_CONTRIBUTION Contribution

FIRST_EVENT_DEPOSIT_ENVIRONMENT Event Deposit environment

FIRST_EVENT_DEPOSIT_GROUP Event Deposit group

FIRST_EVENT_DEPOSIT_TYPE Event Deposit type

FIRST_AGE_MA Age (Ma)

FIRST_AGE_ERROR Age error

FIRST_AGE_TYPE Age type

FIRST_AGE_BASIS Age basis

FIRST_MINERAL_DATED Mineral dated

FIRST_AGE_SYSTEM Age system

FIRST_RADIOMETRIC_AGE_TYPE Radiometric Age type

FIRST_AGE_INSTRUMENTATION Age instrumentation

FIRST_AGE_CONFIDENCE Age confidence

SECOND_EVENTNO Event number

SECOND_EVENT_NAME Event name

SECOND_EVENT_TIMING Event timing

SECOND_CONTRIBUTION Contribution

SECOND_EVENT_DEPOSIT_ENVIRONMENT Event Deposit environment

SECOND_EVENT_DEPOSIT_GROUP Event Deposit group

SECOND_EVENT_DEPOSIT_TYPE Event Deposit type

SECOND_AGE_MA Age (Ma)

SECOND_AGE_ERROR Age error

SECOND_AGE_TYPE Age type

SECOND_AGE_BASIS Age basis

SECOND_MINERAL_DATED Mineral dated

SECOND_AGE_SYSTEM Age system

SECOND_RADIOMETRIC_AGE_TYPE Radiometric Age type

SECOND_AGE_INSTRUMENTATION Age instrumentation

SECOND_AGE_CONFIDENCE Age confidence

THIRD_EVENTNO Event number

THIRD_EVENT_NAME Event name

THIRD_EVENT_TIMING Event timing

THIRD_CONTRIBUTION Contribution

THIRD_EVENT_DEPOSIT_ENVIRONMENT Event Deposit environment

THIRD_EVENT_DEPOSIT_GROUP Event Deposit group

THIRD_EVENT_DEPOSIT_TYPE Event Deposit type

THIRD_AGE_MA Age (Ma)

THIRD_AGE_ERROR Age error

THIRD_AGE_TYPE Age type

THIRD_AGE_BASIS Age Basis

THIRD_MINERAL_DATED Mineral dated

THIRD_AGE_SYSTEM Age system

THIRD_RADIOMETRIC_AGE_TYPE Radiometric Age type

THIRD_AGE_INSTRUMENTATION Age Instrumentation

THIRD_AGE_CONFIDENCE Age confidence

ENDOWMENT_TONNAGE_MT Tonnage (Mt)

ENDOWMENT_BRINE_VOLUME_MM3 Brine volume (Mm3)

CU_PERCENT Percentage of Copper

ZN_PERCENT Percentage of Zinc

PB_PERCENT Percentage of Lead

AG_GRAMS_PER_TONNE Silver (Grams Per Tonne)

AU_GRAMS_PER_TONNE Gold (Grams Per Tonne)

BARITE_PERCENT Percentage of Barite

SB_PERCENT Percentage of Antimony

CD_PERCENT Percentage of Cadmium

SN_PERCENT Percentage of Tin

WO3_PERCENT Percentage of Tungsten Trioxide

MO_PERCENT Percentage of Molybdenum

RE_GRAMS_PER_TONNE Rhenium (Grams Per Tonne)

IN_GRAMS_PER_TONNE Indium (Grams Per Tonne)

F_PERCENT Percentage of Fluorine

BI_PERCENT Percentage of Bismuth

TA_GRAMS_PER_TONNE Tantalum (Grams Per Tonne)

NB_PERCENT Percentage of Niobium

LI2O_PERCENT Percentage of Lithium Oxide

REO_PERCENT Percentage of Rare Earth Oxides

Y_PERCENT Percentage of Yttrium

HF_PERCENT Percentage of Hafnium

U3O8_KILOGRAMS_PER_TONNE Triuranium octoxide (kilograms per tonne)

NI_PERCENT Percentage of Nickel

CO_PERCENT Percentage of Cobalt

PT_GRAMS_PER_TONNE Platinum (Grams Per Tonne)

PD_GRAMS_PER_TONNE Palladium (Grams Per Tonne)

RH_GRAMS_PER_TONNE Rhodium (Grams Per Tonne)

IR_GRAMS_PER_TONNE Iridium (Grams Per Tonne)

OS_GRAMS_PER_TONNE Osmium (Grams Per Tonne)

ZRN_PERCENT Percentage of Zircon

FE_PERCENT Percentage of Iron

V2O5_PERCENT Percentage of Vanadium Pentoxide

SC_KILOGRAMS_PER_TONNE Scandium (Kilograms Per Tonne)

CR2O3_PERCENT Percentage of Chromic Oxide

MG_PERCENT Percentage of Magnesium

MN_PERCENT Percentage of Manganese

AL2O3_PERCENT Percentage of Aluminium Oxide

DIAMOND_CARATS_PER_TONNE Diamond Carats Per Tonne

HEAVY_MINERALS_PERCENT Percentage of Heavy Minerals

P2O5_PERCENT Percentage of Phosphate

SALT_PERCENT Percentage of Salt

K_PERCENT Percentage of Potassium

GRAPHITE_PERCENT Percentage of Graphite

CAF2_PERCENT Percentage of Calcium Fluoride

CU_MEGATONNES Copper Megatonnes

ZN_MEGATONNES Zinc Megatonnes

PB_MEGATONNES Lead Megatonnes

AG_KILOTONNES Silver Kilotonnes

AU_TONNES Gold Tonnes

BARITE_MEGATONNES Barite Megatonnes

SB_KILOTONNES Antimony Kilotonnes

CD_KILOTONNES Cadmium Kilotonnes

SN_KILOTONNES Tin Kilotonnes

WO3_KILOTONNES Tungsten Trioxide Kilotonnes

MO_KILOTONNES Molybdenum Kilotonnes

RE_MEGATONNES Rhenium Megatonnes

IN_KILOTONNES Indium Kilotonnes

F_KILOTONNES Fluorine Kilotonnes

BI_KILOTONNES Bismuth Kilotonnes

TA_KILOTONNES Tantalum Kilotonnes

NB_KILOTONNES Niobium Kilotonnes

LI_KILOTONNES Lithium Kilotonnes

REO_MEGATONNES Rare Earth Oxides Megatonnes

Y_MEGATONNES Yttirum Megatonnes

HF_MEGATONNES Hafnium Megatonnes

U3O8_TONNES Triuranium Octoxide Megatonnes

NI_MEGATONNES Nickel Megatonnes

CO_KILOTONNES Cobalt Kilotonnes

PT_TONNES Platnum Tonnes

PD_TONNES Palladium Tonnes

RH_TONNES Rhodium Tonnes

IR_TONNES Iridium Tonnes

OS_TONNES Osmium Tonnes

ZR_MEGATONNES Zirconium Megatonnes

FE_MEGATONNES Iron Megatonnes

V2O5_KILOTONNES Vanadium Oxide Kilotonnes

SC_TONNES Scandium Tonnes

CR2O3_MEGATONNES Chromic Oxide Megatonnes

MG_MEGATONNES Magnesium Megatonnes

MN_MEGATONNES Manganese Megatonnes

AL2O3_GIGATONNES Aluminium Oxide Gigatonnes

DIAMOND_MEGACARATS Diamond Mega Carat

HEAVY_MINERALS_MEGATONNES Heavy Minerals Megatonnes

P2O5_MEGATONNES Phosphate Megatonnes

SALT_MEGATONNES Salt Megatonnes

K2SO4_KILOTONNES Potassium Sulfate Kilotonnes

GR_MEGATONNES Graphite Megatonnes

FL_KILOTONNES Fluorite Megatonnes

FIRST_HOST_ROCK_STRATNO Host Rock Stratigraphic Index Number (STRANTNO)

FIRST_HOST_ROCK_PID Host Rock Persistent ID (PID)

FIRST_HOST_ROCK_NAME Host Rock Name

FIRST_HOST_ROCK_DESCRIPTION Host Rock Description

FIRST_HOST_ROCK_AGE Host Rock Age

SECOND_HOST_ROCK_STRATNO Host Rock Stratigraphic Index Number (STRANTNO)

SECOND_HOST_ROCK_PID Host Rock Persistent ID (PID)

SECOND_HOST_ROCK_NAME Host Rock Name

SECOND_HOST_ROCK_DESCRIPTION Host Rock Description

SECOND_HOST_ROCK_AGE Host Rock Age

THIRD_HOST_ROCK_STRATNO Host Rock Stratigraphic Index Number (STRANTNO)

THIRD_HOST_ROCK_PID Host Rock Persistent ID (PID)

THIRD_HOST_ROCK_NAME Host Rock Name

THIRD_HOST_ROCK_DESCRIPTION Host Rock Description

THIRD_HOST_ROCK_AGE Host Rock Age

FOURTH_HOST_ROCK_STRATNO Host Rock Stratigraphic Index Number (STRANTNO)

FOURTH_HOST_ROCK_PID Host Rock Persistent ID (PID)

FOURTH_HOST_ROCK_NAME Host Rock Name

FOURTH_HOST_ROCK_DESCRIPTION Host Rock Description

FOURTH_HOST_ROCK_AGE Host Rock Age

FIFTH_HOST_ROCK_STRATNO Host Rock Stratigraphic Index Number (STRANTNO)

FIFTH_HOST_ROCK_PID Host Rock Persistent ID (PID)

FIFTH_HOST_ROCK_NAME Host Rock Name

FIFTH_HOST_ROCK_DESCRIPTION Host Rock Description

FIFTH_HOST_ROCK_AGE Host Rock Age

SIXTH_HOST_ROCK_STRATNO Host Rock Stratigraphic Index Number (STRANTNO)

SIXTH_HOST_ROCK_PID Host Rock Persistent ID (PID)

SIXTH_HOST_ROCK_NAME Host

The Australian Offshore Mineral Locations map shows mineral occurrences and deposits within Australia's 200 nautical mile exclusive economic zone and extended continental shelf.

Australia will have one of the largest marine jurisdictions in the world (14.4 million square kilometres) if the United Nations Commission on the Limits of the Continental Shelf agrees to Australia's submission on the outer limit of its extended continental shelf. This is greater than Australia's total land area (13.6 million square kilometres), including Antarctica.

The Offshore Mineral Locations map sheds light on the mineral prospectivity in this exciting, but poorly known frontier. It should serve also to ensure mineral values are considered in marine planning and decision making.

The Australian Offshore Mineral Locations map draws together data from published and unpublished marine research surveys as well as reports from federal and state government records.

Mineral locations shown include manganese nodules and crusts, shellsand, construction aggregate, heavy mineral sand, phosphorites, diamonds, tin, copper, gold and coal.

Types of mineralisation, some interpreted from limited information, provide an insight into the nature of the depositional settings.

Bathymetry shows the variable physiography of the seafloor that surrounds Australia. For the first time it is possible to identify features such as the contextual setting of manganese crusts and nodules on the East Tasman Plateau and South Tasman Rise, and shellsand and cobalt crust on the edge of the Ceduna Terrace where it descends to the South Australian Abyssal Plain.

Insets and images on the map show further detail, mineral specimens and operational aspects associated with exploration and recovery of marine minerals.

The map is the result of a collaborative project between Geoscience Australia, CSIRO's Wealth from Oceans Flagship and Division of Exploration and Mining, and each of the State and Northern Territory Geological Surveys.

The Australian Offshore Mineral Locations data can be viewed online by using Geoscience Australia's Australian Marine Spatial Information System (AMSIS). AMSIS contains more than 80 layers of Australian marine information which can be viewed and integrated with mineral locations data to create maps to meet specific requirements.

Multispectral remote sensing data acquired by the Landsat 8 Operational Land Imager (OLI) sensor were analyzed using a new, automated technique to generate a map of exposed mineral and vegetation groups in the western San Juan Mountains, Colorado and the Four Corners Region of the United States (Rockwell and others, 2021). Spectral index (e.g. band-ratios) results were combined into displayed mineral and vegetation groups using Boolean algebra. New analysis logic has been implemented to exploit the coastal aerosol band in Landsat 8 OLI data and identify concentrations of iron sulfate minerals. These results may indicate the presence of near-surface pyrite, which can be a potential non-point source of acid rock drainage. Map data, in ERDAS IMAGINE (.img) thematic raster format, represent pixel values with mineral and vegetation group classifications, and can be queried in most image processing and GIS software packages. Rockwell, B.W., Gnesda, W.R., and Hofstra, A.H., 2021, Improve ...

This Open-File report is a digital geologic map database. This digital map database is compiled from previously published sources combined with some new mapping and modifications in nomenclature. The geologic map database delineates map units that are identified by general age and lithology following the stratigraphic nomenclature of the U. S. Geological Survey.

Reflectance data from HyMap™ were processed using the Material Identification and Characterization Algorithm (MICA), a module of the USGS PRISM (Processing Routines in IDL for Spectroscopic Measurements) software (Kokaly, 2011), programmed in Interactive Data Language (IDL; Harris Geospatial Solutions, Broomfield, Colorado). The HyMap reflectance data are provided and described in this data release. MICA identifies the spectrally predominant mineral(s) in each pixel of imaging spectrometer data by comparing continuum-removed spectral features in the pixel’s reflectance spectrum to continuum-removed absorption features in reference spectra of minerals, vegetation, water, and other materials. Linear continuum removal is a technique to isolate an absorption feature from background spectral variations (Clark and Roush, 1984). Following continuum removal of a spectral feature in a reference spectrum and the corresponding channels in an imaging spectrometer pixel, the coefficient of determin ...

WMS service contains records of Historical mining maps stored in the archive CGS in Kutná Hora.

Interactive maps and downloadable data for regional and global Geology, Geochemistry, Geophysics, and Mineral Resources, provided by USGS. Multiple useful links for materials to help understand the geology of locations.

Abstract The Mineral Potential web service provides access to digital datasets used in the assessment of mineral potential in Australia. The service includes maps showing the potential for carbonatite-related rare earth element mineral systems in Australia. Maps showing the potential for carbonatite-related rare earth element (REE) mineral systems in Australia. Model 2 integrates four components: sources of metals, energy drivers, lithospheric architecture, and ore deposition. Supporting datasets including the input maps used to generate the mineral potential maps, an assessment criteria table that contains information on the map creation, and data uncertainty maps are available here Uncertainty Maps. The data uncertainty values range between 0 and 1, with higher uncertainty values being located in areas where more input maps are missing data or have unknown values. Map images provided in the extended abstract have the same colour ramp and equalised histogram stretch, plus a gamma correction of 0.5 not present in the web map service maps, which was applied using Esri ArcGIS Pro software. The extended abstract is avalable here Alkaline Rocks Atlas Legend

Currency Date modified: 16 August 2023 Next modification date: As Needed Data extent Spatial extent North: -9° South: -44° East: 154° West: 112° Source Information Catalog entry: Carbonatite-related rare earth element mineral potential maps Lineage Statement Product Created 20 April 2023 Product Published 16 August 2023 A large number of published datasets were individually transformed to summarise our current understanding of the spatial extents of key mineral system mappable criteria. These individual layers were integrated using statistically derived importance weightings combined with expert reliability weightings within a mineral system component framework to produce national-scale mineral potential assessments for Australian carbonatite-related rare earth element mineral systems. Contact Geoscience Australia, clientservices@ga.gov.au

The five regional compilations of major mineral deposits that are here combined were originally created to begin a global mineral resource assessment, and so should be understood as providing generalized fundamental information about where in the world important mineral resources have been discovered.For our purposes, we did not need to obtain highly precise geographic locations or details of the geometry of the deposits or their precise geographic extents. The user should expect these point locations to be near the deposits they describe, but the locations may be expected to be one or a few kilometers from the actual locations.Likewise this survey did not require detailed information on the geological setting of each deposit or the extent of production or resource estimates. For larger deposits described here, such information may be available in other USGS databases or publications.

Following a commission from the Department of the Environment, the British Geological Survey and the Geological Survey of Northern Ireland produced a series of Mineral Resources Maps of Northern Ireland. The maps are intended to assist strategic decision-making in respect of mineral extraction and the protection of important mineral resources against sterilisation. Six digitally generated maps at a scale of 1:100 000 scale are available. The data were produced by the collation and interpretation of mineral resource data principally held by the Geological Survey of Northern Ireland.

This digital map portrays the bedrock geology of the states of Michigan, Wisconsin, and Minnesota taken from the most recent published regional compilations. Some minor modifications and generalizations have been made from the published maps. Information on mineral deposits of the three states is from the U.S. Geological Survey's Mineral Resource Data System (MRDS). Version 3.0 supercedes the original report released in 1997. It differs from the original map in having expanded attribute information assigned to geologic units and updated shoreline and state boundaries. The new attributes allow expanded capabilities for producing derivative maps for attributes including stratigraphy, lithology, and tectonic settings. The new shoreline and state bounaries offer greater geographic accuracy than the originally published version.

Map of clay minerals – kaolinite, illite and smectite in Western Victoria Project: Understanding Soils and Farming Systems The objective of the study was to quantify clay mineral abundance using quantitative XRD analysis with MIR spectroscopy to formulate predictive models. This was implemented using an MIR spectral library, linked to georeferenced soil sites, to map the spatial occurrence and quantity of clay minerals (kaolinite, illite and smectite) in western Victoria,Australia. Spatial covariates used to derive maps according to GlobalSoilMap specifications are appraised for their connections with clay mineral distribution and relationship to soil forming factors.

Spatial coverage index compiled by East View Geospatial of set "Guinea 1:200,000 Scale Series VZG Mineral Resource Maps". Source data from VOT (publisher). Type: Geoscientific - Geology. Scale: 1:200,000. Region: Africa.