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Graham W. Lederer

Publications and source records attributed to Graham W. Lederer.

At least 19 recordsLinked to original sources
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

Artificial intelligence strategy for the U.S. Geological Survey

Artificial intelligence (AI) can offer opportunities to enhance the science, science delivery, and business operations of the U.S. Geological Survey (USGS). Although USGS staff have proactively adopted AI into our workflows for many years, a comprehensive USGS strategy for AI has not previously been developed. The strategy described here is motivated by the acceleration of AI technological development, the benefits of increased AI adoption to USGS mission delivery as anticipated by USGS staff, rising public concern about the implications and trustworthiness of AI, and emerging Federal directives and guidance about AI. The USGS vision is to continue integrating AI to deliver valuable science for the public good while maintaining high ethical standards, scientific quality and integrity, and compliance with Federal and U.S. Department of the Interior requirements. To realize this vision, the USGS can take steps to (1) develop a strong AI workforce, (2) adapt our organizational approaches to include AI governance and communication, (3) ensure responsible and trustworthy use of AI, (4) modernize our computing and data infrastructure to support AI, and (5) accelerate AI adoption and innovation in the Bureau.

Circular↗
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

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↗
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

Synergy between geology and geophysics in graphite mineral resource assessment

Graphite is designated as a critical mineral by the U.S. Government due to its essential role in modern technology and its vulnerability to supply chain disruption. To evaluate domestic graphite resources, the U.S. Geological Survey (USGS) conducted two airborne electromagnetic (AEM) surveys as part of the Earth Mapping Resources Initiative (Earth MRI) over prospective areas in the southern Appalachians of Alabama and the Seward Peninsula of Alaska. These surveys aimed to refine areas of interest by revealing subsurface electrical resistivity patterns indicative of graphite-bearing rocks. The results of the AEM surveys were integrated with existing geological knowledge and supplemented by field observations to assess the likelihood of undiscovered resources in these regions. This multidisciplinary approach combined geophysical techniques with traditional geological methods, enhancing the understanding of graphite potential in areas that may have been previously overlooked. The findings from this initiative are expected to contribute significantly to the ongoing efforts to secure a reliable domestic supply of graphite, thereby reducing import reliance and supporting national interests in mineral resource management.

Conference Paper↗
USGS2025

Geology and geochemistry of Ni-Co laterite occurrences of northern California and southern Oregon

Ni-Co laterite occurrences in the Klamath Mountains of southern Oregon and northern California contain Ni, Co, and Sc values comparable to global deposits. However, they are thin and limited in extent, often occurring as landslides and slumps. Olivine in the parent material contains Ni and Co and enstatite contains Sc. Through pervasive weathering, these elements where concentrated in Fe- and Mn-oxides. Unlike the mined Nickel Mountian deposit near Riddle, Oregon, these occurrences have no hydrous-Mg silicate minerals. Although the region experienced pervasive weathering in the Eocene, preservation of the laterite profiles was limited in the Klamath Mountains due to accelerated uplift and erosion compared to the Coast Range Provence.

Conference Paper↗
USGS2023

Applications of natural language processing to geoscience text data and prospectivity modelling

Geological maps are powerful models for visualizing the complex distribution of rock types through space and time. However, the descriptive information that forms the basis for a preferred map interpretation is typically stored in geological map databases as unstructured text data that are difficult to use in practice. Herein we apply natural language processing (NLP) to geoscientific text data from Canada, the U.S., and Australia to address that knowledge gap. First, rock descriptions, geological ages, lithostratigraphic and lithodemic information, and other long-form text data are translated to numerical vectors, i.e., a word embedding, using a geoscience language model. Network analysis of word associations, nearest neighbors, and principal component analysis are then used to extract meaningful semantic relationships between rock types. We further demonstrate using simple Naive Bayes classifiers and the area under receiver operating characteristics plots (AUC) how word vectors can be used to: (1) predict the locations of “pegmatitic” (AUC = 0.962) and “alkalic” (AUC = 0.938) rocks; (2) predict mineral potential for Mississippi-Valley-type (AUC = 0.868) and clastic-dominated (AUC = 0.809) Zn-Pb deposits; and (3) search geoscientific text data for analogues of the giant Mount Isa clastic-dominated Zn-Pb deposit using the cosine similarities between word vectors. This form of semantic search is a promising NLP approach for assessing mineral potential with limited training data. Overall, the results highlight how geoscience language models and NLP can be used to extract new knowledge from unstructured text data and reduce the mineral exploration search space for critical raw materials.

Natural Resources Research↗
USGS2023

Rock-to-metal ratios of the rare earth elements

The relative quantities of ore mined and waste rock (i.e., overburden) removed to produce the rare earth elements—their rock-to-metal ratios—were calculated for 21 individual operations or regions covering nearly all mine production in 2018. The results indicate that the rock-to-metal ratios for the total rare earth elements ranged from a low of 1.6 × 10 1 to a high of 3.6 × 10 3 , with operations in Brazil and Russia having the lowest ratios and ion-adsorption clays operations in China and Myanmar having the highest. For comparison, the global average rock-to-metal ratio for the total rare earth elements (9.8 × 10 2 ) fell between that of cobalt (8.6 × 10 2 ) and tungsten (1.1 × 10 3 ). Driven by their relative abundance in the ore and unit prices that were used in the economic allocation of the environmental burdens, the global rock-to-metal ratio for individual rare earth elements was lowest for cerium (2.3 × 10 1 ) and lanthanum (7.7 × 10 1 ) and highest for dysprosium (1.7 × 10 4 ), terbium (3.7 × 10 4 ), and lutetium (6.4 × 10 4 ). Like the rock-to-metal ratios for the total rare earth elements, rock-to-metal ratios for individual rare earth elements varied by roughly two orders of magnitude among the various operations examined. An alternative perspective of only accounting for the overburden that is physically removed in ion-adsorption clays in-situ operations yielded global rock-to-metal ratios that were an order of magnitude lower or less for many of the rare earth elements.

Journal of Cleaner Production↗
USGS2023

Automated georeferencing and feature extraction of geologic maps and mineral sites

The predictive power of mineral prospectivity analysis depends on high quality, spatially accurate, analysis-ready datasets. Of paramount importance are geologic maps and mineral site data, but the state of readiness for utilizing these datasets remains sub-optimal for advanced computational techniques. As the U.S. Geological Survey (USGS) fulfils its mission to map the distribution of critical mineral commodities, non-georeferenced maps held within historical collections represent rich sources of input data. Through a series of machine learning challenges organized by the Defense Advanced Research Projects Agency (DARPA) in collaboration with the USGS, significant progress has been made in accelerating data ingestion, processing, and preparation tasks that enable mineral prospectivity mapping and mineral resource assessment workflows. Specifically, two tasks that previously required time-intensive human effort, 1) georeferencing map images, and 2) legend-based feature extraction from map images, are discussed.

Conference Paper↗
USGS2022

Rock-to-metal ratio: A foundational metric for understanding mine wastes

The quantity of ore mined and waste rock (i.e., overburden or barren rock) removed to produce a refined unit of a mineral commodity, its rock-to-metal ratio (RMR), is an important metric for understanding mine wastes and environmental burdens. In this analysis, we provide a comprehensive examination of RMRs for 25 commodities for 2018. The results indicate significant variability across commodities. Precious metals like gold have RMRs in the range of 10 5 –10 6 , while iron ore and aluminum are on the order of 10 1 . The results also indicate significant variability across operations for a single commodity. The interquartile range of RMRs for individual cobalt operations, for example, varies from 465 to 2157, with a global RMR of 859. RMR variability is mainly driven by ore grades and revenue contribution. The total attributable ore mined and waste rock removed in the production of these 25 commodities sums to 37.6 billion metric tons, 83% of which is attributable to iron ore, copper, and gold. RMRs provide an additional dimension for evaluating the impact of materials and material choice trade-offs. The results can enhance life cycle inventories and be extended to evaluate areas of surface disturbances, mine tailings, energy requirements, and associated greenhouse gas emissions.

Environmental Science & Technology↗
USGS2021

Tungsten skarn mineral resource assessment of the Great Basin region of western Nevada and eastern California

A new quantitative mineral resource assessment for tungsten, a critical mineral commodity with highly concentrated production and a moderate risk of global supply disruption, was conducted for the Great Basin region of western Nevada and eastern California. This assessment was part of a larger effort focusing on three regions in the United States and represents the first study of domestic tungsten resources and mineral potential in over twenty years. By integrating geology, bedrock and stream sediment geochemistry, geophysics, and remote sensing data with recently developed software tools and analyses, estimates of undiscovered tungsten skarn deposits in permissive tracts are combined with grade and tonnage distributions of known deposits to generate probabilistic estimates of undiscovered resources. Identified resources in the Great Basin region total 168 thousand metric tons (kt) of tungsten trioxide (WO 3 ), including 116 kt of past production and 52 kt remaining in place. Consistent with the historic significance of the Great Basin region as a past producer containing a large portion of U.S. identified resources, undiscovered resources are likely to occur adjacent to known deposits and prospects and at unexplored depths. Undiscovered deposits are estimated to contain median resources of 940 kt of WO 3 with a 90% probability of at least 420 kt and a 10% probability of at least 1.7 million metric tons (Mt), of which 240 kt to 1.1 Mt may be economic to extract. Based on a 20-year average price, median recoverable undiscovered resources are estimated at 570 kt WO 3 equivalent with a net present value of $3 billion U.S. dollars. The methods, data, results, and economic significance of the assessment contribute to a scientific understanding of a critical mineral resource with direct implications for policy and land management decisions.

California, Nevada↗
USGS2021

USGS 2020 critical minerals review

Concerns about the lack of domestic production and availability of essential mineral raw materials were a recurring theme throughout the 20th century, particularly in the periods around armed conflicts (Congressional Research Service, 2019). These concerns resulted in the designation of particular minerals as “strategic” or “critical,” terms that commonly depend on the perspective of the user, but which are generally understood to relate to mineral commodities with uses in essential applications, and which are exposed to risk of supply disruptions, with attendant implications for economic and national security interests.

Mining Engineering↗
USGS2020

Grade and tonnage model for tungsten skarn deposits—2020 update

This report presents an updated grade and tonnage model for tungsten skarn deposits. As a critical component of the U.S. Geological Survey’s three-part form of quantitative mineral resource assessment, robust grade and tonnage models are essential to transforming mineral resource assessments into effective tools for decision makers. Using the best data available at the time of publication, this represents the first attempt in nearly 30 years to capture current mineral inventory and cumulative production data for worldwide tungsten skarn deposits. The accuracy of modern assessments of undiscovered tungsten skarn resources is highly influenced by the use of current data on the distribution of the grades and tonnages of well-explored tungsten skarn deposits. Primary factors affecting the changes to these distributions in the model presented here compared with those of previous models are the inclusion of important deposits, especially those in China that had been omitted in previous models; expanded mineral inventories resulting from increased exploration; and changes to international reporting standards. These factors have resulted in dramatic increases in average ore tonnage and slight decreases in the average grade of tungsten skarn deposits compared with previous models. Large increases in contained metal are observed among many of the individual deposits incorporated within this model that were also included in previous tungsten skarn grade and tonnage models. This report also provides recommendations for input parameters related to grade and tonnage models to use with software tools designed to facilitate the three-part form of quantitative mineral resource assessments.

Scientific Investigations Report↗
USGS2020

Evaluating the mineral commodity supply risk of the U.S. manufacturing sector

Trade tensions, resource nationalism, and various other factors are increasing concerns regarding the supply reliability of nonfuel mineral commodities. This is especially the case for commodities required for new and emerging technologies ranging from electric vehicles to wind turbines. In this analysis, we utilize a conventional risk-modeling framework to develop and apply a new methodology for assessing the supply risk to the U.S. manufacturing sector. Specifically, supply risk is defined as the confluence of three factors: the likelihood of a foreign supply disruption, the dependency of U.S. manufacturers on foreign supplies, and the ability of U.S. manufacturers to withstand a supply disruption. The methodology is applied to 52 commodities for the decade spanning 2007-2016. The results indicate that a subset of 23 commodities, including cobalt, niobium, rare earth elements, and tungsten, pose the greatest supply risk. Importantly, this supply risk is dynamic, shifting with changes in global market conditions.

Science Advances↗