This data release provides the descriptions of approximately 20 U.S. sites that include mineral regions, mines, and mineral occurrences (deposits and prospects) that contain enrichments of lithium (Li). This release includes sites that have a contained resource and (or) past production of lithium metal greater than 15,000 metric tons. Sites in this database occur in Arkansas, California, Nevada, North Carolina, and Utah. There are several deposits that were not included in the database because they did not meet the cutoff requirement, and those occur in Arizona, Colorado, the New England area, New Mexico, South Dakota, and Wyoming. In the United States, lithium was first mined from pegmatite orebodies in South Dakota in the late 1800s. The Kings Mountain pegmatite belt of North Carolina also had significant production from pegmatites, and the area may still contain as much as 750 million metric tons (Mt) of ore containing 5 Mt lithium metal (Kesler and others, 2012). In 2018, U.S. production of lithium was restricted to a single lithium-brine mining operation in Nevada. In 2018, the U.S. had a net import reliance as a percentage of apparent consumption of more than 50 percent for lithium (U.S. Geological Survey, 2019). The U.S. is not a significant producer of lithium, so the commodity is mainly imported from Chile and Argentina to meet consumer demand. Lithium is necessary for strategic, consumer, and commercial applications. The primary uses for lithium are in batteries, ceramics, glass, metallurgy, pharmaceuticals, and polymers (U.S. Geological Survey, 2019). Lithium has excellent electrical conductivity and low density (lithium metal will float on water), making it an ideal component for battery manufacturing. Lithium is traded in three primary forms: mineral concentrates, mineral compounds (from brines), and refined metal (electrolysis from lithium chloride). Lithium mineralogy is diverse; it occurs in a variety of pegmatite minerals such as spodumene, lepidolite, amblygonite, and in the clay mineral hectorite. Current global production of lithium is dominated by pegmatite and closed-basin brine deposits, but there are significant resources in lithium-bearing clay minerals, oilfield brines, and geothermal brines (Bradley and others, 2017). The entries and descriptions in the database were derived from published papers, reports, data, and internet documents representing a variety of sources, including geologic and exploration studies described in State, Federal, and industry reports. Resources extracted from older sources might not be compliant with current rules and guidelines in minerals industry standards such as National Instrument 43-101 (NI 43-101) or the Joint Ore Reserves Committee Code (JORC Code). The inclusion of a particular lithium mineral deposit in this database is not meant to imply that the deposit is currently economic. Rather, these deposits were included to capture the characteristics of the larger lithium deposits in the United States, which are diverse in their geology and resource potential. Inclusion of material in the database is for descriptive purposes only and does not imply endorsement by the U.S. Government. The authors welcome additional published information in order to continually update and refine this dataset. Bradley, D.C., Stillings, L.L., Jaskula, B.W., Munk, LeeAnn, and McCauley, A.D., 2017, Lithium, chap. K of Schulz, K.J., DeYoung, J.H., Jr., Seal, R.R., II, and Bradley, D.C., eds., Critical mineral resources of the United States—Economic and environmental geology and prospects for future supply: U.S. Geological Survey Professional Paper 1802, p. K1–K21, https://doi.org/10.3133/pp1802K. Kesler, S.E., Gruber, P.W., Medina, P.A., Keoleian, G.A., Everson, M.P., and Wallington, T.J., 2012, Global lithium resources—relative importance of pegmatite, brine and other deposits: Ore Geology Reviews, v. 48, October ed., p. 55—69. U.S. Geological Survey, 2019, Mineral commodity summaries 2019:

The data included here were used to evaluate the prospectivity for lithium in brines of playas of the western part of the Basin and Range Physiographic Province of the United States. Prospectivity is derived from the mappable criteria used in the descriptive deposit model published by Bradley and others (2013) and focused mainly from the remote sensing point of view. The playas in the study area have been ranked according to size (compared to Clayton Valley, the only area where lithium from brines is being produced in the country), the presence and abundance of source rocks, vegetation (as an indicator of water), reported prospects, and remote sensing data. The remote sensing products used are from data acquired by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor because it has regional coverage not available with other sensors. New in this version: Four records in the Playas feature class ( and the corresponding shapefile and csv files) were modified, affecting the Prospects, Score, and Rank fields.

The product data are six statistics that were estimated for the chemical concentration of lithium in the soil C horizon of the conterminous United States. The estimates are made at 9998 locations that are uniformly distributed across the conterminous United States. The six statistics are the mean for the isometric log-ratio transform of the concentrations, the equivalent mean for the concentrations, the standard deviation for the isometric log-ratio transform of the concentrations, the probability of exceeding a concentration of 55 milligrams per kilogram, the 0.95 quantile for the isometric log-ratio transform of the concentrations, and the equivalent 0.95 quantile for the concentrations. Each statistic may be used to generate a statistical map that shows an attribute of the distribution of lithium concentration.

Global demand for lithium, the primary component of lithium-ion batteries, greatly exceeds known supplies and this imbalance is expected to increase as the world transitions away from fossil fuel energy sources. The goal of this work was to calculate the total lithium mass in brines of the Reynolds oolite unit of the Smackover Formation in southern Arkansas using predicted lithium concentrations from a machine-learning model. This research was completed collaboratively between the U.S. Geological Survey and the Arkansas Department of Energy and Environment—Office of the State Geologist. The Smackover Formation is a laterally extensive petroleum and brine system in the Gulf Coast region that includes locally high concentrations of bromide and lithium in southern Arkansas. This data release contains input files, Python scripts, and an R script used to prepare input files, create a random forest (RF) machine-learning model to predict lithium concentrations, and compute uncertainty in brines of the Reynolds oolite unit of the Smackover Formation in southern Arkansas. This data release also contains a Python script to calculate the total mass of lithium in brines of the Reynolds oolite unit of the Smackover Formation in southern Arkansas based on porosity. Knowledge of data-science and Python and R programming languages is a prerequisite for executing the workflow associated with this product. Users can execute the scripts to prepare input data, train a RF machine-learning model, compute uncertainty, and calculate lithium mass. Explanatory variables used to train the RF model included geologic, geochemical, and temperature data from either published datasets or created and documented in this data release and the associated companion publication (Knierim and others, 2024). See the associated metadata for details. This data release also includes output files (csvs [comma-delimited, plain-text] and rasters [geospatial grids]) of lithium concentration predictions from the RF model, uncertainty ranges, and lithium mass. The depth of prediction of lithium concentration represents the mid-point depth of the Reynolds oolite unit which varies between approximately 3,500 and 11,300 feet deep (below land-surface datum) and 0 and 400 feet thick across the model domain. For a full explanation of methods and results, see the companion manuscript Knierim and others (2024).

Global demand for lithium, the primary component of lithium-ion batteries, greatly exceeds known supplies and this imbalance is expected to increase as the world transitions away from fossil fuel energy sources. The goal of this work was to calculate the total lithium mass in brines of the Reynolds oolite unit of the Smackover Formation in southern Arkansas using predicted lithium concentrations from a machine-learning model. This research was completed collaboratively between the U.S. Geological Survey and the Arkansas Department of Energy and Environment—Office of the State Geologist. The Smackover Formation is a laterally extensive petroleum and brine system in the Gulf Coast region that includes locally high concentrations of bromide and lithium in southern Arkansas. This data release contains input files, Python scripts, and an R script used to prepare input files, create a random forest (RF) machine-learning model to predict lithium concentrations, and compute uncertainty in brines of the Reynolds oolite unit of the Smackover Formation in southern Arkansas. This data release also contains a Python script to calculate the total mass of lithium in brines of the Reynolds oolite unit of the Smackover Formation in southern Arkansas based on porosity. Knowledge of data-science and Python and R programming languages is a prerequisite for executing the workflow associated with this product. Users can execute the scripts to prepare input data, train a RF machine-learning model, compute uncertainty, and calculate lithium mass. Explanatory variables used to train the RF model included geologic, geochemical, and temperature data from either published datasets or created and documented in this data release and the associated companion publication (Knierim and others, 2024). See the associated metadata for details. This data release also includes output files (csvs [comma-delimited, plain-text] and rasters [geospatial grids]) of lithium concentration predictions from the RF model, uncertainty ranges, and lithium mass. The depth of prediction of lithium concentration represents the mid-point depth of the Reynolds oolite unit which varies between approximately 3,500 and 11,300 feet deep (below land-surface datum) and 0 and 400 feet thick across the model domain. For a full explanation of methods and results, see the companion manuscript Knierim and others (2024).

This U.S. Geological Survey (USGS) data release provides the descriptions of the only U.S. sites-including mining districts, mineral occurrences, and mine features-that have reported production and (or) resources of indium (In). This dataset contains the Bingham and West Desert deposits in Utah, and the Chino site in New Mexico. Indium is considered a critical and strategic mineral because of its use in the aerospace, defense, energy, and telecommunications sectors. The primary applications are flat-panel displays, and specialty alloys (Fortier and others, 2018). In 2021, the U.S. was 100 percent net import reliant on indium from China, Canada, Republic of Korea, and France (U.S. Geological Survey, 2022). Indium is most commonly recovered from sphalerite, a zinc-sulfide mineral, wherein the indium occurs in quantities of less than 1 part per million (ppm) to 100 ppm (U.S. Geological Survey, 2022). In the U.S., indium is found in porphyry and skarn deposits. The West Desert deposit in Utah is the only deposit in the U.S. with a modern National Instrument 43-101 (NI 43-101) compliant resource estimate of indium (Dyer and others, 2014). The entries and descriptions in the database were derived from published papers, reports, data, and internet documents representing a variety of sources, including geologic and exploration studies described in State, Federal, and industry reports. Resources extracted from older sources might not be compliant with current rules and guidelines in minerals industry standards such as NI 43-101. The presence of an indium mineral deposit in this database is not meant to imply that the deposit is currently economic. Rather, these deposits were included to capture the characteristics of the largest indium deposits in the United States. Inclusion of material in the database is for descriptive purposes only and does not imply endorsement by the U.S. Government. The authors welcome additional published information in order to continually update and refine this dataset. Dyer, T.L., Tietz, P.G., and Austin, J.B., 2014, Technical report on the West Desert zinc-copper-indium-magnetite project, preliminary economic assessment, Juab County, Utah, prepared for InZinc Mining Ltd. [Filing date March 17, 2014]: Mine Development Associates, 188 p., accessed March 10, 2020, at http://www.sedar.com. Fortier, S.M., Nassar, N.T., Lederer, G.W., Brainard, J., Gambogi, J., and McCullough, E.A., 2018, Draft critical mineral list-Summary of methodology and background information-U.S. Geological Survey technical input document in response to Secretarial Order No. 3359: U.S. Geological Survey Open-File Report 2018-1021, 15 p., https://doi.org/10.3133/ofr20181021. U.S. Geological Survey, 2022, Mineral commodity summaries 2022: U.S. Geological Survey, 202 p., https://doi.org/10.3133/mcs2022.