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USGS2026

Rare earth element potential in coal and coal ash in the U.S. Gulf Coast

United States heavy reliance on imports of critical minerals (CMs), including rare earth elements (REEs), underscores the importance of development of domestic sources. The study objective was to quantify CM and REE concentrations in coal and coal ash in the US Gulf Coast region. CM and REE concentrations were measured for 118 samples from outcrops and 14 mines in the Gulf Coast. Results show that total REE + Yttrium (REY) concentrations (dry coal basis) are comparable to those of the upper continental crust (UCC) with localized hot spots, such as the Texas Gibbons Creek mine (REY ≤ ~ 2860 ppm). When normalized to UCC REY concentration (169 ppm, dry coal basis), REY to UCC ratios for Gulf Coast coal samples range from 0.1 to 17 (median ratio 0.6). REE extractability from lignites is high (median: 63%–93%) using environmentally benign weak acid. In addition to raw coal, coal ash from power plants could also serve as an REE source with a median ratio of REY in ash relative to coal of 4; however, extractability from coal ash is generally much lower (≤ 5% using the same weak acid as in coal). The median basket price for extracted REY as oxides from coal, assuming 70% extractability, is $3.2 per tonne of coal and $186 billion based on 58 billion metric tonnes of dry coal in the Gulf Coast. REEs important for magnets (Pr + Nd + Tb + Dy) account for ~ 80% of the total value. The corresponding median basket price for extracted REY as oxides from coal ash, assuming ~ 30% extractability, is ~$4.4 per tonne of ash and $1.2 billion based on 258 million tonnes of ash. REE production from coal would likely require co-products, such as activated carbon or humic acids, to attain economic viability. Production of REEs from coal ash could offset remediation costs related to potential water contamination. This reconnaissance study shows the potential for REE production from coal and coal ash in the Gulf Coast; however, carbon coproducts and/or societal benefits would likely be required for socioeconomic viability.

Alabama, Florida, Louisiana, Mississippi, Texas↗

USGS2024

Separating signals in elevation data improves supervised machine learning predictions for hydrothermal favorability

A recent study identified topography (land surface elevation above sea level) as an important input dataset (feature) for predicting the location of hydrothermal systems in the Great Basin in Nevada. Yet, topography is generally a result of more than one geological process and may consequently contain multiple distinct signals. For example, the geologic evolution of the Great Basin has produced both crustal thickening (i.e., regional-scale trends in elevation) and thinning via Basin and Range extensional faulting (i.e., valley-scale topographic relief). We postulate that these geologic processes may affect the occurrence of hydrothermal systems differently. Therefore, we separate the regional trend from the valley-scale signal in the Great Basin, and then use them separately to evaluate the importance of each as predictors for hydrothermal favorability. Our prior work applying supervised machine learning (ML) using the data from the Nevada Machine Learning Project demonstrated that employing a training strategy that randomly selects negative training sites produces better performing models for predicting hydrothermal favorability than a training strategy that uses expert-selected negatives. The models created using both training strategies exhibited a west-east geographic trend in the predictions for the favorability of hydrothermal resources. These models generally predicted higher favorability in western Nevada and lower favorability in eastern Nevada. This west-east trend in predicted favorability correlates with elevation across the Great Basin, which trends higher from west to east. By separating the original elevation feature into distinct features for elevation trend (i.e., regional-scale topography) and detrended elevation (i.e., valley-scale or local relative topography), we find that models using the separated topographic signals consistently outperform competing models that use the original elevation feature. Although western Nevada still exhibits higher favorability than eastern Nevada, using separated signals for regional elevation and local structure reduces the west-east prediction trend in the region and emphasizes structures associated with hydrothermal upflow. This work emphasizes how carefully engineering features to represent geological conditions relevant to hydrothermal systems allows ML algorithms to detect important patterns for predicting hydrothermal resource favorability and leads to better model performance.

Conference Paper↗

USGS2025

Paleoproterozoic vein graphite mineralization caused by decarbonation in the Ruby Range, Montana, USA

Hydrothermal graphite veins are a possible source for modern battery materials and require better understanding of their carbon source(s) and absolute timing to develop mapable criteria for exploration models. We present new observations of graphite vein and alteration paragenesis and U-Pb LA-ICP-MS titanite age data from the Ruby prospect, Montana, USA, that constrain mineralization timing and source. The graphite veins cut high-temperature metamorphic rocks of the lower Christensen Range suite and are associated with intense diopside (Di0.69Hd0.27Jo0.04) alteration of marble. The oldest titanite ages in fresh marble and partially altered calc-silicate gneiss are ca. 2,500 – 2,450 Ma, show elevated REE values, and Eu/Eu* <1.5, consistent with growth during early regional metamorphism. Titanite in diopside-graphite alteration, interpreted as part of the hydrothermal vein-forming episode, cluster around 1,750 Ma, are characterized by lower REE values, and show Eu/Eu* >2; suggesting a low fO 2 fluid generated from metamorphism during the Big Sky orogeny. Our paragenetic observations and titanite ages indicate graphite vein formation via skarnoid decarbonation reactions in marble late in regional orogenesis. Granulite-facies, carbonate-bearing, supracrustal rock terranes are thus favorable for hydrothermal graphite vein deposits.

Montana↗

USGS2026

Ice sheet dynamics drive pronounced changes in the subsurface freshwater-saltwater interface

Saltwater is migrating into freshwater aquifers globally with water quality and biogeochemical implications, yet saltwater intrusion in glaciated regions is sparsely investigated. Field observations suggest that groundwater head in glaciated systems is influenced by ice sheet forcings and provides evidence that seawater infiltrated into offshore aquifers during past deglaciation events. To understand links between ice sheet dynamics, groundwater head, and saltwater intrusion, we use numerical models to explore the effects of deglaciation on nearshore head and salinity distributions. We find that ice sheet thinning diminishes groundwater head, and the resulting shift in subsurface pressure gradients drives rapid landward movement of the subsurface freshwater-saltwater interface up to 4.0 km or 1.3 m per m ice sheet loss. Results highlight an overlooked saltwater intrusion mechanism that aligns with field observations and affects glaciated coastlines undergoing ice sheet retreat, underscoring the need to consider this mechanism in studies of contemporary coastal water quality.

Geophysical Research Letters↗

USGS2024

U.S. Geological Survey Karst Interest Group proceedings, Nashville, Tennessee, October 22-24, 2024

Karst hydrogeologic systems represent challenging and unique conditions to scientists studying groundwater flow and contaminant transport. Karst terrains are characterized by distinct and beautiful landscapes, caverns, and springs, and many of the exceptional karst areas are designated as national or state parks. The range and complexity of landforms and groundwater flow systems associated with karst terrains are enormous, perhaps more than any other aquifer type. The U.S. Geological Survey (USGS) Karst Interest Group (KIG), formed in 2000, is a loosely knit, grassroots organization of USGS and non-USGS scientists and researchers devoted to fostering better communication among scientists working on, or interested in, karst aquifers. The primary mission of the KIG is to encourage and support interdisciplinary collaboration and technology transfer among scientists working in karst areas. To accomplish its mission, the KIG has organized a series of workshops. To date (2024), nine KIG workshops, including the workshop documented in this report, have been held. The abstracts and extended abstracts provide a snapshot in time of past and current karst related studies. The USGS Water Availability and Use Science Program funded the workshop and proceedings. The planning committee for the ninth workshop includes Thomas D. Byl (USGS and Tennessee State University), Allan K. Clark (USGS), Laura M. DeMott (USGS), Eve L. Kuniansky (USGS, Emeritus), Benjamin V. Miller (USGS), and Lawrence E. Spangler (USGS, Emeritus). The workshop proceedings are edited by Eve L. Kuniansky and Lawrence E. Spangler. The field trip guide was produced by Benjamin V. Miller and Brian Ham (Tennessee Department of Environment and Conservation) and included in the proceedings from the KIG’s 2021 virtual workshop to be used on the optional field trip held on Thursday, October 24, 2024.

Open-File Report↗

USGS2025

Properties of solid bitumen formed during hydrous, anhydrous, and brine pyrolysis of oil shale: Implications for solid bitumen reflectance in source-rock reservoirs

Solid bitumen reflectance (BR o in %) is widely used as a thermal maturity proxy in source-rock reservoirs, yet solid bitumen texture and physical attributes may be affected by varying environmental constraints during its formation, e.g., water saturation, mineral catalysis, or salinity. Herein we investigated the development of solid bitumen properties during artificial maturation using three diverse (lacustrine to marine) oil shale samples containing abundant amorphous H-rich sedimentary organic matter (bituminite). The oil shales were treated via pyrolysis (320 °C, 72 h) using hydrous, anhydrous, and brine conditions, causing the development of a newly formed solid bitumen in the experiment residues from the thermal conversion of bituminite. The properties of the newly formed solid bitumen then were evaluated via geochemical screening tests, optical and electron microscopy, and infrared spectroscopy. Experimental residues also were treated via solvent extraction, allowing characterization of the effects of extraction on solid bitumen. Results showed that bituminite with higher resin and asphaltene components evolved to a solid bitumen with higher reflectance (as a percentage of the original value) when exposed to the same heat treatment, relative to bituminite with higher saturate and aromatic components. Aromatization of solid bitumen also was generally more pronounced in the presence of deionized water relative to anhydrous conditions, supporting prior observations. These results suggest the compositions of primary sedimentary organic matter, and the local concentration of water, affect the development of solid bitumen during thermal advance, potentially explaining the origin of multiple solid bitumen populations with varying reflectance in source-rock reservoir samples or in a single microscope field. Experiments using brine were inconclusive regarding enhanced/decreased aromatization, which could be attributed to the salinities of the brines used in the tests. Extraction of residues caused a consistent increase in BR o values, suggesting that migrating or expelled oils could cause an increase in BR o via natural solvation and absent an increase in temperature. This work provides insights into the development and evolution of BR o in source-rock reservoirs as a function of the composition of the original bituminite and changing environmental conditions, with potentially broad application for petroleum prospecting and resource estimation.

Colorado↗

USGS2024

U.S. Geological Survey Groundwater Climate Response Network—2023

As of October 2023, the U.S. Geological Survey (USGS) operated more than 660 sites across the United States and its territories as part of the Groundwater Climate Response Network (CRN). The CRN is comprised of wells and springs selected to monitor the effects of climate variability, such as droughts, on groundwater levels and spring discharge nationwide. The CRN includes more than 550 locations with realtime data and more than 100 sites with non-real-time data available to the public on the CRN web mapper and the USGS National Water Dashboard.

General Information Product↗

USGS2024

Neogene hydrothermal Fe- and Mn-oxide mineralization of Paleozoic continental rocks, Amerasia Basin, Arctic Ocean

Rocks dredged from water depths of 1,605, 2,500, 3,300, and 3,400 m in the Arctic Ocean included Paleozoic continental rocks pervasively mineralized during the Neogene by hydrothermal Fe and Mn oxides. Samples were recovered in three dredge hauls from the Chukchi Borderland and one from Mendeleev Ridge north of Alaska and eastern Siberia, respectively. Many of the rocks were so pervasively altered that the protolith could not be identified, while others had volcanic, plutonic, and metamorphic protoliths. The mineralized rocks were cemented and partly to wholly replaced by the hydrothermal oxides. The Amerasia Basin, where the Chukchi Borderland and Mendeleev Ridge occur, supports a series of faults and fractures that serve as major zones of crustal weakness. We propose that the stratabound hydrothermal deposits formed through the flux of hydrothermal fluids along Paleozoic and Mesozoic faults related to block faulting along a rifted margin during minor episodes of Neogene tectonism and were later exposed at the seafloor through slumping or other gravity processes. Tectonically driven hydrothermal circulation most likely facilitated the pervasive mineralization along fault surfaces via frictional heating, hydrofracturing brecciation, and low- to moderate temperature Fe- and Mn-rich hydrothermal fluids, which mineralized the crushed, altered, and brecciated rocks.

Geochemistry, Geophysics, Geosystems↗

USGS2024

A benchmark dataset and workflow for landslide susceptibility zonation

Landslide susceptibility shows the spatial likelihood of landslide occurrence in a specific geographical area and is a relevant tool for mitigating the impact of landslides worldwide. As such, it is the subject of countless scientific studies. Many methods exist for generating a susceptibility map, mostly falling under the definition of statistical or machine learning. These models try to solve a classification problem: given a collection of spatial variables, and their combination associated with landslide presence or absence, a model should be trained, tested to reproduce the target outcome, and eventually applied to unseen data. Contrary to many fields of science that use machine learning for specific tasks, no reference data exist to assess the performance of a given method for landslide susceptibility. Here, we propose a benchmark dataset consisting of 7360 slope units encompassing an area of about 4,100 km 2 "> 4,100 km 2 in Central Italy. Using the dataset, we tried to answer two open questions in landslide research: (1) what effect does the human variability have in creating susceptibility models; (2) how can we develop a reproducible workflow for allowing meaningful model comparisons within the landslide susceptibility research community. With these questions in mind, we released a preliminary version of the dataset, along with a “call for collaboration,” aimed at collecting different calculations using the proposed data, and leaving the freedom of implementation to the respondents. Contributions were different in many respects, including classification methods, use of predictors, implementation of training/validation, and performance assessment. That feedback suggested refining the initial dataset, and constraining the implementation workflow. This resulted in a final benchmark dataset and landslide susceptibility maps obtained with many classification methods. Values of area under the receiver operating characteristic curve obtained with the final benchmark dataset were rather similar, as an effect of constraints on training, cross–validation, and use of data. Brier score results show larger variability, instead, ascribed to different model predictive abilities. Correlation plots show similarities between results of different methods applied by the same group, ascribed to a residual implementation dependence. We stress that the experiment did not intend to select the “best” method but only to establish a first benchmark dataset and workflow, that may be useful as a standard reference for calculations by other scholars. The experiment, to our knowledge, is the first of its kind for landslide susceptibility modeling. The data and workflow presented here comparatively assess the performance of independent methods for landslide susceptibility and we suggest the benchmark approach as a best practice for quantitative research in geosciences.

Earth-Science Reviews↗

USGS2025

The role of bedrock circulation depth and porosity in mountain streamflow response to prolonged drought

Quantitative understanding is lacking on how the depth of active groundwater circulation in bedrock affects mountain streamflow response to a multi‐year drought. We use an integrated hydrological model to explore the sensitivity of a variety of streamflow metrics to bedrock circulation depth and porosity under a plausible extreme drought scenario lasting up to 5 years. Endmember depth versus hydraulic conductivity relationships and porosity values for fractured crystalline rock are simulated. With drought, a deeper circulation system with higher drainable porosity more effectively buffers minimum flow and significantly limits perennial stream loss in comparison to a shallow circulation system. Streamflow buffering is accomplished through extensive groundwater storage loss. However, deeper circulation systems experience prolonged recovery from drought in comparison to storage‐limited shallow systems. Research highlights the importance of characterizing the deeper bedrock hydrogeology in mountainous watersheds to better understand and predict drought impacts on stream ecosystem health and water resource sustainability.

Colorado↗

USGS2024

Self-guided decision support groundwater modelling with Python

The GMDSI tutorial notebooks repository provides learners with a comprehensive set of tutorials for self-guided training on decision-support groundwater modelling using Python-based tools. Although targeted at groundwater modelling, they are based around model-agnostic tools and readily transferable to other environmental modelling workflows. The tutorials are divided into three parts. The first covers fundamental theoretical concepts. These are intended as background reading for reference on an as-needed basis. Tutorials in the second part introduce learners to some of the core concepts parameter estimation in a groundwater modelling context, as well as providing a gentle introduction to the PEST, PEST++ and pyEMU software. Lastly, the third part demonstrates how to implement highly-parameterized applied decision-support modelling workflows. The tutorials aim to provide examples of both “how to use” the software as well as “how to think” about using the software. A key advantage to using notebooks in this context is that the workflows described run the same code as practitioners would run on a large-scale real- world application. Using a small synthetic model facilitates rapid progression through the workflow.

Journal of Open Source Education↗

USGS2023

Don’t Let Negatives Hold You Back: Accounting for Underlying Physics and Natural Distributions of Hydrothermal Systems When Selecting Negative Training Sites Leads to Better Machine Learning Predictions

Selecting negative training sites is an important challenge to resolve when utilizing machine learning (ML) for predicting hydrothermal resource favorability because ideal models would discriminate between hydrothermal systems (positives) and all types of locations without hydrothermal systems (negatives). The Nevada Machine Learning project (NVML) fit an artificial neural network to identify areas favorable for hydrothermal systems by selecting 62 negative sites where the research team had confidence that no hydrothermal resource exists. Herein, we compare the implications of the expert selection of negatives (i.e., the NVML strategy) with a random sample strategy, where it is assumed that areas outside the favorable structural ellipses defined by NVML are negative. Because hydrothermal systems are sparse, it is highly probable that, in the absence of a favorable geological structure, hydrothermal favorability is low. We compare three training strategies: 1) the positive and negative labeled examples from NVML; 2) the positive examples from NVML with randomly selected negatives in equal frequency as NVML; and 3) the positive examples from NVML with randomly selected negatives reflecting the expected natural distribution of hydrothermal systems relative to the total area. We apply these training strategies to the NVML feature data (input data) using two ML algorithms (XGBoost and logistic regression) to create six favorability maps for hydrothermal resources. When accounting for the expected natural distribution of hydrothermal systems, we find that XGBoost performs better than the NVML neural network and its negatives. Model validation was less reliable using F1 scores, a common performance metric, than comparing probability estimates at known positives, likely because of the extreme natural class imbalance and the lack of negatively labeled sites. This work demonstrates that expert selection of negatives for training in NVML likely imparted modeling bias. Accounting for the sparsity of hydrothermal systems and all the types of locations without hydrothermal systems allows us to create better models for predicting hydrothermal resource favorability.

Geothermal Resources Council Transactions↗

USGS2026

Deaf, deafblind, and hard of hearing university student experiences with earthquake early warning in the United States: Evaluating language planning and technology access

The growing literature on deaf and hard of hearing (DHH+) populations and disasters demonstrates that emergency communication (including alerts) is not reaching global DHH + individuals with dangerous impacts for morbidity and mortality. This is the first research study in the U.S. to qualitatively explore the experiences of DHH + persons with earthquake early warning (EEW) through group-based dialogue sessions. The study investigates eight DHH + university students'past earthquake experiences, access to EEW alerts, and perceptions of ShakeAlert Ⓡ , an EEW system for detecting earthquakes and alerting residents of California, Oregon, and Washington. Findings highlight key gaps in disaster alert usability within four thematic areas: lack of messaging in participants' language(s), unclear alert messaging, deficient message delivery mechanisms for deafblind persons, and insufficient access to earthquake information and training that leads to dependence on informal information networks. Weaknesses identified in these four themes reduce DHH + trust in EEW systems and compromise the capacity of alert recipients to take swift protective action or to mentally prepare before shaking starts. The study also underscores structural factors such as insufficient linguistic representation in disaster language planning and technology design, which ignores the linguistic and sensory access needs of DHH + individuals. Building on disaster language planning frameworks, we recommend involving DHH + populations to co-develop EEW alerts. By centering DHH + perspectives, this research contributes to ongoing efforts to ensure that EEW systems reach everyone.

California↗

USGS2025

Detecting hidden sedimentary geothermal systems in the Upper Colorado River Basin

Geothermal resources exist in sedimentary rock where circulation of water for efficient extraction or storage of heat is possible. Except in rare instances where hot water is expressed at the land surface, sedimentary geothermal resources are hidden, so the identification of these systems is optimally accomplished using predictive subsurface modeling. An integrated approach using detailed paleogeographic interpretations, subsurface geologic mapping, and numerical modeling has produced regional geologic and temperature models for the Upper Colorado River Basin, a large watershed in central North America that contains many sedimentary basins. These models identify areas of hidden sedimentary geothermal resource potential in low temperature (<90°C), moderate temperature (90–150°C), and high temperature (>150°C) fairways across the study area. These models incorporate maps of key horizons in outcrop and the subsurface to create a robust structural framework that can be used to target favorable geology for natural or engineered permeability. This framework is populated with lithologies derived from detailed palaeogeographical maps and over 40,000 bottom hole temperature (BHT) values were used to create a calibrated three-dimensional (3D) temperature model across the region. The resulting maps serve as a regional sedimentary geothermal play fairway screening tool for evaluating different grades of sedimentary geothermal resources and for identifying areas of interest where more detailed, prospect-scale studies can be undertaken.

Arizona, Colorado, New Mexico, Utah, Wyoming↗

USGS2008

Late Jurassic igneous rocks in south-central Arizona and north-central Sonora: Magmatic accompaniment of crustal extension

In Middle Jurassic time, the region of south-central Arizona and north-central Sonora was part of a continental magmatic arc. In early Late Jurassic time, this magmatic arc gradually gave way to a extensional regime. Late Jurassic rifting was accompanied by magmatism, typically small in volume relative to the preceding arc phase, and with mildly alkaline compositions. These igneous rocks, the 158- to 146-Ma Ko Vaya Suite, make up much or most of the Comobabi, Artesa, Quijotoa, and Brownell Mountains and Sierra del Cobre. The bimodal Ko Vaya Suite constitutes volcanic to shallow plutonic complexes comprising chiefly trachyandesite and volcanic wacke; monzodiorite; rhyolite porphyry; distinctive, compositionally and texturally heterogeneous, quartzpoor granite (the Ko Vaya Granite); A-type perthite granite and quartz syenite; aphyric intrusive rhyolite; and locally abundant hematite veins. Alteration, particularly potassic alteration, is widespread. Minimally altered samples of granite and quartz syenite have the composition of trachyte or trachydacite and alkali rhyolite. The Ko Vaya Suite is nonconformably overlain by the latest Jurassic Sand Wells Formation, approximately correlative with the Glance Conglomerate, the basal unit of the Bisbee Group. The Ko Vaya Suite is in some ways analogous to the coeval Independence dike swarm of eastern California, but less diffuse. We infer that the Ko Vaya Suite represents especially intense or focused Late Jurassic extensional magmatism.

Arizona, Sonora↗

USGS2026

Evolution of arsenic speciation during thermal treatment simulating wildfire heating in arsenic-rich sediments

Understanding arsenic transformations during wildfires can help better constrain the environmental impacts of increased wildfire intensity, frequency, and burned area. Previous studies have monitored the evolution of metal(loid) speciation at specific time/temperature endpoints, hampering the comprehension of the dynamic transformations of metal(loid)s during wildfires. Here, a novel approach, namely in situ time-resolved X-ray absorption near edge structure (TR-XANES) spectroscopy, was used for the first time to monitor the heat-induced redox transformations of arsenic (As) in two As-rich sediments with different characteristics. No arsenic transformations were observed in either sediment at room temperature or when the sediments were heated to a maximum temperature of 400 ºC. When heated to a maximum temperature of 600 or 700 ºC, As underwent complex, dynamic, and partially reversible redox transformations, the extent of which varied with sediment properties, initial As speciation, and heating temperature and duration. In the case of sediments initially containing As(V) and a low sulfide/sulfate ratio, the As(V) was reduced to As(III) over a short period of time, followed by immediate reoxidation of As(III) to As(V). The extent of the transient As reduction increased with increasing maximum temperature. The final As speciation at the end of the heating experiment was not substantially different from that at the beginning of the experiment. In contrast, in sediments that were best described by a more complex combination of reference compounds (As(V) coprecipitated with or adsorbed on iron oxide, arseniosiderite, arsenopyrite) with a high sulfide/sulfate ratio, As(V) phases were reduced to As(III) and As(III) was further reduced to As(-I) (at 700 ºC, only), followed by delayed reoxidation of As(-I) to As(III) (at 700 ºC, only) and reoxidation of As(III) to As(V). Approximately 9% and 26% of all As remained as As(III) ( i.e., was not reoxidized to As(V)) at the end of the experiment when heated to a maximum temperature of 600 and 700 ºC, respectively, which is higher than the relative abundance of As(III) in the native sediment. These fire-induced As transformations can potentially enhance As mobilization to surface water and groundwater, posing risks to environmental and human health.

California↗

USGS2026

Investigating the influence of climate and volcanic surface aging on fluvial erosion: A case study of Réunion Island, Indian Ocean

Precipitation is one of the dominant drivers of landscape erosion and evolution; however, the effects of typical rainfall compared with less frequent, high-magnitude precipitation events on erosion remain unclear. Volcanic islands are ideal locations to study such phenomena due to their simple geometries, nontectonic construction, and strong spatiotemporal rainfall gradients. However, spatial variation in surface age, created during their construction, often complicates their degradation histories by introducing temporal changes in erosion rates as drainage networks develop. Réunion Island (western Indian Ocean) presents a clear example of this, with an east–west gradient in both surface age and mean annual precipitation, as well as infrequent cyclones that alter the background rainfall pattern. In this study, we analyze the effects of surface age, average rainfall, and rainfall variability on basin development and fluvial erosion across the island. We calculate basin-averaged values of basin morphology, age, precipitation, river discharge, eroded volumes, and erosion rates, and use these to analyze the dominant drivers of landscape evolution through a series of correlation analyses. Our results indicate a temporal dependence on the influence of precipitation, with young surfaces being dominantly eroded by high-rainfall events and older surfaces eroded by mean annual rainfall patterns. Furthermore, we show that drainage development of shield volcanoes follows similar trends to other volcano types, and suggest that surface permeability and groundwater structure are important controls on runoff-driven erosion on shield volcanoes. These results add new components to the question of how precipitation impacts erosion.

Réunion Island↗

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↗