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Joshua Mark Rosera

Publications and source records attributed to Joshua Mark Rosera.

7 recordsLinked to original sources
USGS2026

Global pegmatite-hosted lithium, cesium, and rubidium resources: A dataset for grade and tonnage modeling

Quantitative mineral resource assessments of potential undiscovered deposits can inform future mineral supply scenarios, but their accuracy is conditional on building robust grade and tonnage models of known deposits. This study presents an up-to-date global compilation and analysis of recently discovered and original, in-situ pegmatite-hosted Li, Cs, and Rb resources prior to historic production. Our analysis yields a median tonnage of 21.2 million tons (Mt) and grade of 1.12% Li 2 O, respectively, for global Li pegmatite deposits ( n = 73). The grades and tonnages of Li pegmatite resources vary depending on the age of the bedrock host domain, pegmatite crystallization age, and primary ore mineralogy. Lithium pegmatite resources hosted in Archean to transitional Archean-Paleoproterozoic domains have the largest median tonnage (29.8 Mt; n = 38), and those hosted in Paleoproterozoic to Mesoproterozoic domains have smaller median tonnages (6.5 Mt; n = 16). Cesium deposits where pollucite is the primary ore mineral have a bimodal grade distribution, with modes of 2.40 and 0.035 wt% Cs 2 O for high- and low-grade deposits, respectively, while Rb deposits are more unimodal with a median grade of 0.247 wt% Rb 2 O. Pegmatite-hosted Cs and Rb resources have median tonnages of 7.6 and 6.3 Mt, respectively. Covariation between ore mineralogy and the degree of crustal enrichment in pegmatite-hosted deposits is diagnostic of petrogenetic differences, including melt source characteristics, magma evolution, or variable degrees of volatile solubility. The Li pegmatite compilation is suitable for fitting robust numerical models to support quantitative assessments. More well-defined Rb and Cs pegmatite resources are required for quantitative assessments, but these data provide useful information about original in-place resources for framing supply discussions.

Ore Geology Reviews↗
USGS2026

Quantitative mineral resource assessment of lithium pegmatite deposits in the southern Appalachian orogen

The first quantitative mineral resource assessment for undiscovered lithium pegmatite deposits in the southern Appalachian region of the United States was conducted. Permissive tracts for lithium pegmatite deposits were delineated by integrating lithological, tectonic, geochemical, geophysical and mineral occurrence data. Lithium pegmatite prospectivity of the tracts was ranked with simplified mappable criteria, including proximity to Paleozoic felsic intrusions and major lithotectonic structures, stream sediment geochemical anomalies, and pegmatite occurrence data. The geospatial data and permissive tracts were used to estimate the number of undiscovered lithium pegmatite deposits. These estimates were integrated into probabilistic simulations along with a new global lithium pegmatite grade and tonnage dataset to quantify potential contained undiscovered lithium resources. An economic filter was applied to convert the probabilistic estimates of contained lithium into recoverable material. The identified lithium pegmatite resources for the Carolina Lithium and Kings Mountain deposits, North Carolina, contain 1589 thousand tons (kt) of Li 2 O. The median contained undiscovered resource for the southern Appalachian orogen was estimated to be 2240 kt Li 2 O. At 90% confidence, the region contains at least 130 kt Li 2 O, and 10,700 kt at 10% confidence. After applying economic filters, the median recoverable contained resource was 1430 kt Li 2 O, corresponding to approximately 201 years of current lithium imports for consumption in the United States. North and South Carolina are likely to contain most of these resources. Coarse data resolution and intra-state variations in the geological data contribute to uncertainty of undiscovered lithium pegmatite resources. Continued efforts to harmonize disparate geospatial datasets with updated or new information can improve the accuracy and precision of estimated undiscovered lithium pegmatite resources in the study area and at broader scales.

Alabama, Georgia, Maryland, North Carolina, South ↗
USGS2026

Top-down targeted network analysis of critical mineral commodities applied to international geochemistry database

The global demand for critical mineral commodities is rapidly increasing, making domestic production an important factor in supporting the economy and national security. Large scale, publicly available geochemical databases allow for the application of data informatics methods to interrogate critical mineral commodities data for correlations in deposit formation and distribution, particularly for identifying enrichment of multiple critical mineral commodities at the same deposit. In this study, we applied network analysis to the Critical Minerals Mapping Initiative (CMMI) ore geochemistry (Critical Minerals in Ores, CMiO) database to identify the high concentration (defined as 10× bulk crustal abundance) co-occurrence of different critical mineral commodities across a mineral system hierarchy from deposit environments to individual deposits. Identifying patterns or unique outliers in enrichment in network communities will allow for the location of secondary critical mineral commodity resources from under-utilized deposits. We find trends in the enrichment of critical mineral commodities in network-communities between the elements praseodymium (Pr), neodymium (Nd), terbium (Tb), and dysprosium (Dy) across multiple CMiO database deposit environments and groups down to specific deposit types and sites. A separate trend in network community deposition is observed as well between iridium (Ir) and platinum (Pt) in deposit environments, groups, types, and sites. Network analysis focused on critical minerals in magmatic-hydrothermal deposits identified multiple deposit sites from different deposit types within the CMiO database with concentrations of Dy, Nd, Tb, Pr, Ir, and Pt that are at least ten times greater than the crustal average. This approach can be applied to any target element(s) or deposit(s) of interest, allowing broad investigation of co-enriched critical mineral commodities.

Journal of Geochemical Exploration↗
USGS2025

Statistical approaches for modeling correlated grade and tonnage distributions and applications for mineral resource assessments

Correlations between grade and tonnage exist in mineral resource data compiled from published reports, but they are not always addressed during quantitative assessment of undiscovered mineral resources. Failure to account for correlated grade and tonnage distributions can result in geologically unrealistic assessment results. Current software tools simulate univariate ore tonnage and multivariate resource grades of undiscovered deposits independently. As a result, analysts are forced to rely on ad-hoc solutions to minimize the correlation issues by: 1) creating subsets of data with restricted criteria; 2) truncating grade and tonnage distributions; and 3) testing model robustness using exploratory data analysis. While these methods represent pragmatic solutions, the statistical solutions presented here provide additional options to address real correlations in grade and tonnage data used for mineral resource assessments. We present a modified version of the MapMark4 package in R that introduces two alternatives for modeling grade and tonnage distributions, consisting of a multivariate solution that accounts for correlations between ore tonnage and metal grades and an empirical solution that utilizes simple random sampling with replacement to reproduce coupled grades and tonnages from the input data. We present simulations for contained ore and metal for three case studies representing tungsten skarn, komatiite-hosted nickel, and sediment-hosted carbonate amagmatic zinc-lead (Mississippi Valley-type) deposits. Employing the methods presented here yields quantitative mineral resource assessment results that more closely reflect the empirical distributions of grades and tonnages observed in nature and expands the applicability of these tools for ongoing critical mineral resource assessments.

Applied Computing and Geosciences↗
USGS2025

Quantitative mineral resource assessment of lithium pegmatite deposits in the Appalachian Orogen, USA

Lithium is classified as a U.S. critical mineral commodity, and its demand is projected to drastically increase through 2040, driven by electric vehicle production and energy storage applications (IEA 2021).Most global lithium production is not in the United States increasing vulnerability to a supply disruption. The U.S. Geological Survey is actively assessing domestic lithium deposits including lithium-bearing pegmatites in the Appalachian orogen. Permissive tracts for lithium pegmatite deposits were delineated by integrating lithological, tectonic, geochemical, geophysical, and mineral occurrence data. The geospatial data and permissive tracts were used to estimate the number of undiscovered lithium pegmatite deposits. Estimates were then integrated into probabilistic simulations along with a new global lithium pegmatite grade and tonnage dataset to quantify potential contained undiscovered lithium resources. An economic filter was used to estimate the amount of potentially recoverable undiscovered resources. Preliminary computations for the northern Appalachians, including application of the economic filter to the median recoverable contained resource, yields 900,000 metric tons of Li 2 O that correspond to enough Li 2 O to replace 127 years of import reliance at the current rate (7,100 t Li 2 O/yr; USGS, 2025). For the southern Appalachians, preliminary computations yielded 1,430,000 metric tons of Li 2 O, which corresponds to 201 years of import reliance.

Alabama, Connecticut, Delaware, Georgia, Maine, Ma↗
USGS2025

Extracting data from maps: Lessons learned from the artificial intelligence for critical mineral assessment competition

The U.S. Geological Survey (USGS), Defense Advanced Projects Research Agency (DARPA), NASA Jet Propulsion Laboratory (JPL), and MITRE ran a 12-week machine learning competition aimed at accelerating development of AI tools for critical mineral assessments. The Artificial Intelligence for Critical Mineral Assessment Competition solicited innovative solutions for two challenges: 1) automated georeferencing of historical maps, and 2) automated feature extraction from historical maps. Competitors used a new dataset of historical map images to train, validate, and evaluate their models. Automated georeferencing pipelines attained a median root-mean square error of 1.1 km. Prompt-based extraction (i.e., with user input) of polygons, polylines, and points from geologic maps yielded median F1-scores of 0.77, 0.56, 0.35, respectively. Geologic maps pose numerous challenges for AI workflows because they vary significantly. However, despite its short duration, the competition yielded promising results that have since spurred further innovation in this area and led to the development of new AI tools to semi-automate key, time-consuming parts of the assessment workflow.

Applied Computing and Geosciences↗