Search USGS⌕ Search

SEARCH · Search USGS

Results for “Geosciences”

Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 127 records · Page 7Linked to original sources
USGS2022

Alaska North Slope terrestrial gas hydrate systems: Insights from scientific drilling

A wealth of information has been accumulated regarding the occurrence of gas hydrates in nature, leading to significant advancements in our understanding of the geologic controls on their occurrence in both the terrestrial and marine settings of the Arctic. Gas hydrate accumulations discovered in the Alaska North Slope have been the focus of several important geoscience and production testing research programs. The Mount Elbert Gas Hydrate Stratigraphic Test Well of 2007 yielded one of the most complete geologic datasets on Arctic gas hydrate systems and important reservoir engineering data. The 2011/2012 field test of the Iġnik Sikumi gas hydrate production test well provided important insight into gas hydrate production technologies, yielding additional information on the petrophysical properties of gas hydrate reservoir systems. The Hydrate-01 Stratigraphic Test Well, drilled late in 2018, confirmed the geologic conditions at an Alaska North Slope drill site that was selected for an extended gas hydrate production test. In 2018, the US Geological Survey used information derived from previous scientific drilling programs to assess the volume of undiscovered, technically recoverable gas resources at a mean estimate of about 54 trillion cubic feet (~1.5 trillion cubic meters) within the gas hydrates in the North Slope of Alaska. This assessment has shown that the amount of gas stored as gas hydrates in this area is equal to about half of the known volume of conventional natural gas resources in the region.

Alaska↗
USGS2016

A practical guide to the use of major elements, trace elements, and isotopes in compositional data analysis: Applications for deep formation brine geochemistry

In the geosciences, isotopic ratios and trace element concentrations are often used along with major element concentrations to help determine sources of and processes affecting geochemical variation. Compositional Data Analysis (CoDA) is a set of tools, generally attuned to major element data, concerned with the proper statistical treatment and removal of spurious correlations from compositional data. Though recent insights have been made on the incorporation of trace elements and stable isotope ratios to CoDA, this study provides a general approach to thinking about how radiogenic isotopes, stable isotopes, and trace elements fit with major elements in the CoDA framework. In the present study, we use multiple data sets of deep formation brines and compare traditional mixing models to their CoDA counterparts to examine fluid movement between reservoirs. Concentrations of individual isotopes are calculated using isotopic ratios and global mean isotopic abundances. One key result is that isotope parts (e.g. 18O, 17O, 16O, 2H, 1H, 87Sr, 86Sr) can simply be modelled by the major element concentration (H2O, Sr) in a clr-biplot as they are perfectly dependent. Another important result is that an ilr transformation of radiogenic isotope parts (e.g. 86Sr and 87Sr in 87Sr/86Sr) and trace elements can, like stable isotopes in delta notation, be treated as a linear function of the isotopic ratio or trace element concentration, scaled only by a constant. This implies that there are multiple situations in which an ilr transformation provides little additional insight for the analysis of trends: (1) any two parts with low log ratio variance (e.g. an isotope ratio), no matter their concentrations in the solution, (2) any low concentration parts (trace elements) or a ratio of a trace to a major element, no matter the variance of the elements, and (3) large positive ratios (major/trace) over a restricted range of variance. Similarly, a multivariate ilr transformation of a large data set with many parts will also be a simple perturbation if the balances are evenly split between parts. CoDA transformations, however, even if they do not provide new insight in some specific cases, will provide consistent interpretations for all types of data.

Conference Paper↗
USGS2011

Book review: Extreme ocean waves

‘‘Extreme Ocean Waves’’ is a collection of ten papers edited by Efim Pelinovsky and Christian Kharif that followed the April 2007 meeting of the General Assembly of the European Geosciences Union. A note on terminology: extreme waves in this volume broadly encompass different types of waves, includ- ing deep-water and shallow-water rogue waves (alternatively termed freak waves), storm surges from cyclones, and internal waves. Other types of waves such as tsunamis or rissaga (meteotsunamis) are not discussed in this volume. It is generally implied that ‘‘extreme’’ has a statistical connotation relative to the average or significant wave height specific to each type of wave. Throughout the book, in fact, the reader will find a combination of theoretical and statistical/ empirical treatment necessary for the complete examination of this subject. In the introduction, the editors underscore the importance of studying extreme waves, documenting several dramatic instances of damaging extreme waves that occurred in 2007. Review info: Extreme Ocean Waves . By E. Pelinovsky and C. Kharif (eds), 2008. ISBN: 978-1402083136, xiii, 196 pp.

Pure and Applied Geophysics↗
USGS2017

Book review: Extreme ocean waves

“ Extreme Ocean Waves ”, edited by E. Pelinovsky and C. Kharif, second edition, Springer International Publishing, 2016; ISBN: 978-3-319-21574-7, ISBN (eBook): 978-3-319-21575-4 The second edition of “ Extreme Ocean Waves ” published by Springer is an update of a collection of 12 papers edited by Efim Pelinovsky and Christian Kharif following the April 2007 meeting of the General Assembly of the European Geosciences Union. In this edition, three new papers have been added and three more have been substantially revised. Color figures are now included, which greatly aids in reading several of the papers, and is especially helpful in visualizing graphs as in the paper on symbolic computation of nonlinear wave resonance (Tobisch et al.). A note on terminology: extreme waves in this volume broadly encompass different types of waves, including deep-water and shallow-water rogue waves (which are alternatively termed freak waves), and internal waves. One new paper on tsunamis (Viroulet et al.) is now included in the second edition of this volume. Throughout the book, the reader will find a combination of laboratory, theoretical, and statistical/empirical treatment necessary for the complete examination of this subject. In the Introduction, the editors underscore the importance of studying extreme waves, documenting a dramatic instance of damaging extreme waves that recently occurred in 2014.

Pure and Applied Geophysics↗
USGS2004

A Complex Systems Model Approach to Quantified Mineral Resource Appraisal

For federal and state land management agencies, mineral resource appraisal has evolved from value-based to outcome-based procedures wherein the consequences of resource development are compared with those of other management options. Complex systems modeling is proposed as a general framework in which to build models that can evaluate outcomes. Three frequently used methods of mineral resource appraisal (subjective probabilistic estimates, weights of evidence modeling, and fuzzy logic modeling) are discussed to obtain insight into methods of incorporating complexity into mineral resource appraisal models. Fuzzy logic and weights of evidence are most easily utilized in complex systems models. A fundamental product of new appraisals is the production of reusable, accessible databases and methodologies so that appraisals can easily be repeated with new or refined data. The data are representations of complex systems and must be so regarded if all of their information content is to be utilized. The proposed generalized model framework is applicable to mineral assessment and other geoscience problems. We begin with a (fuzzy) cognitive map using (+1,0,-1) values for the links and evaluate the map for various scenarios to obtain a ranking of the importance of various links. Fieldwork and modeling studies identify important links and help identify unanticipated links. Next, the links are given membership functions in accordance with the data. Finally, processes are associated with the links; ideally, the controlling physical and chemical events and equations are found for each link. After calibration and testing, this complex systems model is used for predictions under various scenarios.

Environmental Management↗
USGS2006

Typing mineral deposits using their associated rocks, grades and tonnages using a probabilistic neural network

A probabilistic neural network is employed to classify 1610 mineral deposits into 18 types using tonnage, average Cu, Mo, Ag, Au, Zn, and Pb grades, and six generalized rock types. The purpose is to examine whether neural networks might serve for integrating geoscience information available in large mineral databases to classify sites by deposit type. Successful classifications of 805 deposits not used in training - 87% with grouped porphyry copper deposits - and the nature of misclassifications demonstrate the power of probabilistic neural networks and the value of quantitative mineral-deposit models. The results also suggest that neural networks can classify deposits as well as experienced economic geologists. ?? International Association for Mathematical Geology 2006.

Mathematical Geology↗
USGS2019

Assessment experimental semivariogram uncertainty in the presence of a polynomial drift

The semivariogram, which measures the spatial variability between experimental data, is generally used as a structural input in all two-point geostatistical procedures. However, in most geoscience applications, experimental semivariograms are usually computed from a limited number of sparsely spaced measurements, which results in uncertainty associated with the semivariance values estimated for a specified number of lags. More importantly, considering a spatial variable modelled by a nonstationary random field, uncertainty is not only in the experimental semivariogram of the residuals, but also in the coefficients of the drift model estimated from the available experimental data. Therefore, when assessing the reliability of an experimental semivariogram (or estimated semivariances) in the nonstationary case, both aforementioned uncertainties should be taken into account. The aim of this paper is to extend the “Generalised Bootstrap” procedure to the nonstationary model by propagating the uncertainty associated with the estimated drift coefficients into the uncertainty in the experimental semivariogram of the residuals. The proposed methodology is demonstrated in a case study using abundant geophysical measurements characterised by a nonstationary random function. Two scenarios are evaluated in the case study: (1) it is assumed that the drift coefficients can be estimated without any uncertainty, and (2) uncertainty of the drift coefficients is taken into account. We have explained the methodology that allows to assess the uncertainty of the semivariogram lag estimates in the presence of the drift in the mean. Considering the second scenario, uncertainty is obviously larger than the case where uncertainty of the drift in the mean is ignored. This evaluation should be considered in applications where the data is often rather limited, such as subsurface hydrology (i.e. porosity, transmissivity), soil science (i.e. heavy metal content, soil moisture) and mining (i.e. scoping or pre-feasibility stage of the project). In fact, modern geostatistics should provide not only the semivariogram estimates but also estimation of its uncertainty.

Natural Resources Research↗
USGS2009

Seismic reflection characteristics of naturally-induced subsidence affecting transportation

High-resolution seismic reflections have been used effectively to investigate sinkholes formed from the dissolution of a bedded salt unit found throughout most of Central Kansas. Surface subsidence can have devastating effects on transportation structures. Roads, rails, bridges, and pipelines can even be dramatically affected by minor ground instability. Areas susceptible to surface subsidence can put public safety at risk. Subsurface expressions significantly larger than surface depressions are consistently observed on seismic images recorded over sinkholes in Kansas. Until subsidence reaches the ground surface, failure appears to be controlled by compressional forces evidenced by faults with reverse orientation. Once a surface depression forms or dissolution of the salt slows or stops, subsidence structures are consistent with a tensional stress environment with prevalent normal faults. Detecting areas of rapid subsidence potential, prior to surface failure, is the ultimate goal of any geotechnical survey where the ground surface is susceptible to settling. Seismic reflection images have helped correlate active subsidence to dormant paleofeatures, project horizontal growth of active sinkholes based on subsurface structures, and appraise the risk of catastrophic failure. ?? China University of Geosciences (Wuhan) and Springer-Verlag GmbH 2009.

Conference Paper↗
USGS2009

High-frequency Rayleigh-wave method

High-frequency (≥2 Hz) Rayleigh-wave data acquired with a multichannel recording system have been utilized to determine shear (S)-wave velocities in near-surface geophysics since the early 1980s. This overview article discusses the main research results of high-frequency surface-wave techniques achieved by research groups at the Kansas Geological Survey and China University of Geosciences in the last 15 years. The multichannel analysis of surface wave (MASW) method is a non-invasive acoustic approach to estimate near-surface S-wave velocity. The differences between MASW results and direct borehole measurements are approximately 15% or less and random. Studies show that simultaneous inversion with higher modes and the fundamental mode can increase model resolution and an investigation depth. The other important seismic property, quality factor ( Q ), can also be estimated with the MASW method by inverting attenuation coefficients of Rayleigh waves. An inverted model (S-wave velocity or Q ) obtained using a damped least-squares method can be assessed by an optimal damping vector in a vicinity of the inverted model determined by an objective function, which is the trace of a weighted sum of model-resolution and model-covariance matrices. Current developments include modeling high-frequency Rayleigh-waves in near-surface media, which builds a foundation for shallow seismic or Rayleigh-wave inversion in the time-offset domain; imaging dispersive energy with high resolution in the frequency-velocity domain and possibly with data in an arbitrary acquisition geometry, which opens a door for 3D surface-wave techniques; and successfully separating surface-wave modes, which provides a valuable tool to perform S-wave velocity profiling with high-horizontal resolution.

Earth Science↗
USGS2009

Land 3D-seismic data: Preprocessing quality control utilizing survey design specifications, noise properties, normal moveout, first breaks, and offset

The recent proliferation of the 3D reflection seismic method into the near-surface area of geophysical applications, especially in response to the emergence of the need to comprehensively characterize and monitor near-surface carbon dioxide sequestration in shallow saline aquifers around the world, justifies the emphasis on cost-effective and robust quality control and assurance (QC/QA) workflow of 3D seismic data preprocessing that is suitable for near-surface applications. The main purpose of our seismic data preprocessing QC is to enable the use of appropriate header information, data that are free of noise-dominated traces, and/or flawed vertical stacking in subsequent processing steps. In this article, I provide an account of utilizing survey design specifications, noise properties, first breaks, and normal moveout for rapid and thorough graphical QC/QA diagnostics, which are easy to apply and efficient in the diagnosis of inconsistencies. A correlated vibroseis time-lapse 3D-seismic data set from a CO 2 -flood monitoring survey is used for demonstrating QC diagnostics. An important by-product of the QC workflow is establishing the number of layers for a refraction statics model in a data-driven graphical manner that capitalizes on the spatial coverage of the 3D seismic data. ?? China University of Geosciences (Wuhan) and Springer-Verlag GmbH 2009.

Conference Paper↗
USGS1993

Geologic research in support of sustainable agriculture

The importance and role of the geosciences in studies of sustainable agriculture include such traditional research areas as, agromineral resource assessments, the mapping and classification of soils and soil amendments, and the evaluation of landscapes for their vulnerability to physical and chemical degradation. Less traditional areas of study, that are increasing in societal importance because of environmental concerns and research into sustainable systems in general, include regional geochemical studies of plant and animal trace element deficiencies and toxicities, broad-scale water quality investigations, agricultural chemicals and the hydrogeologic interface, and minimally processed and ion-exchange agrominerals. We discuss the importance and future of phosphate in the US and world based on human population growth, projected agromineral demands in general, and the unavailability of new, high-quality agricultural lands. We also present examples of studies that relate geochemistry and the hydrogeologic characteristics of a region to the bioavailability and cycling of trace elements important to sustainable agricultural systems.

Agriculture, Ecosystems and Environment↗
USGS2015

Formulating the American Geophysical Union's Scientific Integrity and Professional Ethics Policy: Challenges and lessons learned

Creating an ethics policy for a large, diverse geosciences organization is a challenge, especially in the midst of the current contentious dialogue in the media related to such issues as climate change, sustaining natural resources, and responding to natural hazards. In 2011, the American Geophysical Union (AGU) took on this challenge, creating an Ethics Task Force to update their ethics policies to better support their new Strategic Plan and respond to the changing scientific research environment. Dialogue with AGU members and others during the course of creating the new policy unveiled some of the following issues to be addressed. Scientific results and individual scientists are coming under intense political and public scrutiny, with the efficacy of the science being questioned. In some cases, scientists are asked to take sides and/or provide opinions on issues beyond their research, impacting their objectivity. Pressure related to competition for funding and the need to publish high quality and quantities of papers has led to recent high-profile plagiarism, data fabrication, and conflict of interest cases. The complexities of a continuously advancing digital environment for conducting, reviewing, and publishing science has raised concerns over the ease of plagiarism, fabrication, falsification, inappropriate peer review, and the need for better accessibility of data and methods. Finally, students and scientists need consistent education and encouragement on the importance of ethics and integrity in scientific research. The new AGU Scientific Integrity and Ethics Policy tries to address these issues and provides an inspirational code of conduct to encourage a responsible, positive, open, honest scientific research environment.

Book chapter↗
USGS2026

The Sedimentary Geochemistry and Paleoenvironments Project Phase 2 data release: An open data resource for the study of Earth's environmental history

Geochemical data from sedimentary rocks are the primary source of information regarding Earth's surface evolution through time, including its air and water envelopes and interactions with life and deep Earth processes. The Sedimentary Geochemistry and Paleoenvironments Project (SGP) is a scientific consortium centered around open data and community-driven development of cyberinfrastructure tools and resources for sedimentary geochemistry and Earth history. Here we describe the SGP Phase 2 data release, which focused on incorporating Paleoproterozoic and Mesoproterozoic (2500–1000 million years ago) data and better accommodating carbonate data. This data release was built through the involvement of >200 researchers worldwide in academia, government, and industry, and provides the largest available public data resource for our user community in the academic fields of geochemistry, sedimentology, tectonics, paleontology, Earth history, and paleoclimate, as well as the petroleum and minerals industries. The dataset now encompasses 126,006 samples and 4,132,371 geochemical analyses. In addition to direct entry by SGP Team Members, we have ingested and incorporated datasets from the Geoscience Australia OZCHEM database, the Alberta Geological Survey, and the Deep-Time Marine Sedimentary Element Database (DM-SED) compilation. This paper details sampling in the Phase 2 dataset with respect to age, geography, lithology, and other geological characteristics, documents access via our search website and API, discusses possible issues and/or biases in the dataset that could impact analyses, describes plans for governance and stewardship of data from Indigenous lands, and serves as the citable reference paper for the data release.

Chemical Geology↗
USGS2008

Northeast storms ranked by wind stress and wave-generated bottom stress observed in Massachusetts Bay, 1990-2006

Along the coast of the northeastern United States, strong winds blowing from the northeast are often associated with storms called northeasters, coastal storms that strongly influence weather. In addition to effects caused by wind stress, the sea floor is affected by bottom stress associated with these storms. Bottom stress caused by orbital velocities associated with surface waves integrated over the duration of a storm is a metric of storm strength at the sea floor. Near-bottom wave-orbital velocities calculated by using measurements of significant wave height and dominant wave period and the parametric spectral method described in Wiberg and Sherwood [Wiberg, P.L., Sherwood, C.R. Calculating wave-generated bottom orbital velocities from surface wave parameters. Computers in Geosciences, in press] compared well with observations in Massachusetts Bay. Integrated bottom-wave stress (called IWAVES), calculated at 30 m water depth, and a companion storm-strength metric, integrated surface wind stress at 10 m (called IWINDS), are used to provide an overview of the strength, frequency, and timing of large storms in Massachusetts Bay over a 17-year period from January 1990 through December 2006. These new metrics reflect both storm duration and intensity. Northeast storms were the major cause of large waves in Massachusetts Bay because of the long fetch to the east: of the strongest 10% of storms (n=38) ranked by IWAVES, 22 had vector-averaged wind stress from the northeast quadrant. The Blizzard of December 1992, the Perfect Storm of October 1991, and a December 2003 storm were the strongest three storms ranked by IWAVES and IWINDS, and all were northeasters. IWAVES integrated over the winter season (defined as October-May) ranged by about a factor of 11; the winters with the highest integrated IWAVES were 1992-1993 and 2004-2005 and the winter with the lowest integrated IWAVES was 2001-2002. May 2005 was the only month in the 17-year record that two of the nine strongest northeast storms ranked by IWINDS occurred in the same month or year; these were also the only storms of the nine strongest northeast storms to occur in the spring.

Massachusetts Bay↗
USGS2011

Surficial geology and benthic habitat of the German Bank seabed, Scotian Shelf, Canada

To provide the scientific context for management of a newly opened scallop fishing ground, surficial geology and benthic habitats were mapped on German Bank on the southern Scotian Shelf off Atlantic Canada. To provide a seamless regional dataset, multibeam sonar surveys covered 5320 sqaure kilometres of the bank in water depths of 30–250 m and provided 5 m horizontal resolution bathymetry and backscatter strength. Geoscience data included high-resolution geophysical profiles (seismic reflection and sidescan sonar) and seabed sediment samples. Geological interpretation and is overlain in places by glacial and postglacial sediment. Biological data included seafloor video transects and photographs from which 127 taxa of visible megabenthos were identified. Trawl bycatch data were obtained from government annual research surveys. Statistical analysis of revealed that bedrock is exposed at the seafloor on much of German Bankthese two datasets and a suite of oceanographic environmental variables demonstrated that significantly different fauna exist on bedrock, glacial sediment and postglacial sediment.

German Bank seabed, Scotian Shelf↗
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↗
USGS2024

Pliocene Model Intercomparison Project Phase 3 (PlioMIP3) – Science plan and experimental design

The Pliocene Model Intercomparison Project (PlioMIP) was initiated in 2008. Over two phases PlioMIP has helped co-ordinate the experimental design and publication strategy of the community, which has included an increasing number of climate models and modelling groups from around the world. It has engaged with palaeoenvironmental scientists to foster new data synthesis supporting the construction of new model boundary conditions, as well as to facilitate new data-model comparisons. The work has advanced our understanding of Pliocene climates and environments, enhanced our knowledge regarding the ability of complex climate and Earth System models to accurately simulate climate change, and helped to refine our estimates of how sensitive the climate system is to forcing conditions. In this community protocol paper, we outline the scientific plan for PlioMIP Phase 3 (PlioMIP3). This plan provides the required guidance to participating modelling groups from around the world to successfully set up and perform PlioMIP3 climate model experiments. The project is open to new participants from the scientific community (both from the climate modelling and geosciences communities). In PlioMIP3, we retain the PlioMIP2 Core experiments ( Eoi 400 , E 280 ) and extend the Core requirements to include either an experiment focussed on the Early Pliocene or an alternative Late Pliocene simulation (or both). These additions (a) allow a comparison of Early and Late Pliocene warm intervals and help build research connections and synergy with the MioMIP (Miocene Model Intercomparison Project - also known as DeepMIP-Miocene) and PlioMioVAR projects (Pliocene-Miocene Variability Working Group), and (b) create an alternative time slice simulation for 3.205 Ma (MIS KM5c) through removal of some of the largest palaeogeographic differences introduced between PlioMIP1 and 2 resulting in minimal land-sea mask variations from the modern. In addition, we present ten optional experiments designed to enhance our assessment of climate sensitivity and to explore the uncertainty in greenhouse gas-related forcing. For the first time, we introduce orbital sensitivity experiments into the science plan, as well as simulations incorporating dynamic vegetation-climate feedbacks and an experiment designed to examine the potential significance of East Antarctic Ice Sheet boundary condition uncertainty. These changes enhance palaeo-to-future scientific connections and enable an exploration of the significance of palaeogeographic uncertainties on climate simulations.

Global and Planeatary Change↗
USGS2022

Data-driven prospectivity modelling of sediment-hosted Zn-Pb mineral systems and their critical raw materials

Demand for critical raw materials is expected to accelerate over the next few decades due to continued population growth and the shifting consumption patterns of the global economy. Sedimentary basins are important sources for critical raw materials and new discoveries of sediment–hosted Mississippi Valley–type (MVT) and/or clastic–dominated (CD) Zn–Pb deposits are likely required to mitigate future supply chain disruptions for Zn, Pb, Ag, Cd, Ga, Ge, Sb, and In. Herein we integrate public geoscience datasets using a discrete global grid to system to model the mineral potential for MVT and CD deposits across Canada, the United States of America, and Australia. Statistical analysis of the model results demonstrates that surface–wave tomography and derivative products from satellite gravity datasets can be used to map the most favourable paleo–tectonic settings of MVT and CD deposits inboard of orogenic belts and at the rifted edges of cratonic lithosphere, respectively. Basin development at pre–existing crustal boundaries was likely important for maintaining the low geothermal–gradients that are favourable for metal transport and generating the crustal fluid pathways that were reactivated during ore–formation, as suggested by the statistical association of both sediment–hosted mineral deposit types with the edges of upward–continued gravity and long–wavelength magnetic anomalies. Multivariate statistical analysis demonstrates that the most prospective combination of these geophysical datasets varies for each geological region and deposit type. We further demonstrate that maximum and minimum geological ages, coupled with Phanerozoic paleogeographic reconstructions, represent mappable proxies for the availability of oxidized, brine–generating regions that are the most likely source of ore–forming fluids (e.g., low– to mid–latitude carbonate platforms and evaporites). Ore deposition was likely controlled by interaction between oxidized, low–temperature brines and sulfidic and/or carbonaceous rocks, which, in some cases, can be mapped at the exposed surface or identified using the available rock descriptions. Baseline weights–of–evidence models are based on regional geophysics and are the least impacted by missing surface information but yield relatively poor results, as demonstrated by the low area–under–the–curve (AUC) for the spatially independent test set on the success–rate plot (AUC = 0.787 for MVT and AUC = 0.870 for CD). Model performance can be improved by: (1) using advanced methods that were trained and validated during a series of semi–automated machine learning competitions; and/or (2) incorporating geological and geophysical datasets that are proxies for each component of the mineral system. The best–performing gradient boosting machine models yield higher AUC for the test set (AUC = 0.983 for MVT and AUC = 0.991 for CD) and reduce the search space by >94%. The model results highlight the potential benefits of mapping sediment–hosted mineral systems at continental scale to improve mineral exploration targeting for critical raw materials.

Ore Geology Reviews↗