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Publications

Listed below are publication products directly associated with the Geology, Energy & Minerals Science Center:

Filter Total Items: 1166

Capacity assessment for Earth Monitoring, Analysis, and Prediction (EarthMAP) and future integrated monitoring and predictive science at the U.S. Geological Survey

Executive SummaryManagers of our Nation’s resources face unprecedented challenges driven by the convergence of increasing, competing societal demands and a changing climate that affects the stability, vulnerability, and predictability of those resources. To help meet these challenges, the scientific community must take advantage of all available technologies, data, and integrative Earth systems mo
Authors
Jennifer L. Keisman, Sky Bristol, David S. Brown, Allison K. Flickinger, Gregory L. Gunther, Peter S. Murdoch, MaryLynn Musgrove, John C. Nelson, Gregory D. Steyer, Kathryn A. Thomas, Ian R. Waite

Oil and gas wastewater components alter streambed microbial community structure and function

The widespread application of directional drilling and hydraulic fracturing technologies expanded oil and gas (OG) development to previously inaccessible resources. A single OG well can generate millions of liters of wastewater, which is a mixture of brine produced from the fractured formations and injected hydraulic fracturing fluids (HFFs). With thousands of wells completed each year, safe manag
Authors
Denise M. Akob, Adam Mumford, Andrea Fraser, Cassandra Rashan Harris, William H. Orem, Matthew S. Varonka, Isabelle M. Cozzarelli

Random forest

This entry defines and discusses the random forest machine learning algorithm. The algorithm is used to predict class or quantities for target variables using values of a set of predictor variables. It uses decision trees that are generated from bootstrap sampling of the training data set to create a "forest". The entry discusses the algorithm steps, the interpretative tools of the resulting mode
Authors
Emil D. Attanasi, Timothy Coburn

Constraints on triple oxygen isotope kinetics

Isotopic disequilibrium is not as well constrained as equilibrium, hindering interpretation of isotopic variations. Kinetic isotope effects, a subset of disequilibrium, are common in nature and have been assumed to be distinct from equilibrium and mass independent isotope effects based on underdeveloped criteria. Using basic physical principles, we provide needed mechanistic constraints on mass-de
Authors
Justin A. Hayles, Bryan Alan Killingsworth

Correspondence analysis for mineral commodity research: An example workflow for mineralized calderas, southwest United States

Historical mine and mineral deposit datasets are routinely used to inform quantitative mineral assessment models, but they also can contain a wealth of supplementary qualitative information that is generally underutilized. We present a workflow that uses correspondence analysis, an exploratory tool commonly applied to multivariate abundance data, to better utilize qualitative data in these histori

Authors
Joshua Mark Rosera, Drew S Coleman

Multilayer perceptrons (MLPs)

Artificial neural networks (ANNs) are adaptable systems that can solve problems that are difficult to describe with a mathematical relationship. They seek relationships between different types of datasets with their abilities to learn either with supervision or without. ANNs recognize patterns between input and output space and generalize solutions, in a way simulating the human brain’s learning e
Authors
C. Özgen Karacan

Method for compiling temporally and spatially aggregated data on hydraulic fracturing—Treatments and wells

This report provides a step-by-step method for compiling hydraulic fracturing data in the United States from the IHS Markit, 2019, U.S. Well History and Production Relational Database. Data on hydraulically fractured wells include their location (geologic province, State, county), well type (oil or gas), orientation (directional, horizontal, or vertical), spud date, completion date and the hydraul
Authors
Brian A. Varela, Tanya J. Gallegos

Mapping multivariate ore occurrence data with correspondence analysis

Correspondence analysis is a multivariate method that can be applied to mineral abundance data. Ore mineral assemblages from broadly underutilized prospect and occurrence data can be treated as geochemical anomalies, projected to low-dimensional space, and returned into map view. This approach could have applications for mineral prospectivity mapping and delineation of permissive areas during mi
Authors
Joshua Mark Rosera

Realizations

In statistics, a realization is an observed value of a random variable (Gubner 2006). In mathematical geology, the most important realizations are those in the form of maps of spatially correlated regionalized variables.Spatial description of random variables within complex domains and making certain decisions about those require complete knowledge of the attribute of interest at each point in spa
Authors
C. Özgen Karacan

Diagenetic barite-pyrite-wurtzite formation and redox signatures in Triassic mudstone, Brooks Range, northern Alaska

Mineralogical and geochemical studies of interbedded black and gray mudstones in the Triassic part of the Triassic-Jurassic Otuk Formation (northern Alaska) document locally abundant barite and pyrite plus diverse redox signatures. These strata, deposited in an outer shelf setting at paleolatitudes of ~45 to 60°N, show widespread sedimentological evidence for bioturbation. Barite occurs preferenti
Authors
John F. Slack, Ryan J. McAleer, Wayne (Pat) Shanks, Julie A. Dumoulin

Hydrous pyrolysis of New Albany Shale: A study examining maturation changes and porosity development

The characterization of nanoscale organic structures has improved our understanding of porosity development within source-rock reservoirs, but research linking organic porosity evolution to thermal maturity has generated conflicting results. To better understand this connection, an immature (0.25% solid bitumen reflectance; BRo) sample of the New Albany Shale was used in four isothermal hydrous py
Authors
Brett J. Valentine, Paul C. Hackley, Javin J. Hatcherian

Machine learning can assign geologic basin to produced water samples using major ion geochemistry

Understanding the geochemistry of waters produced during petroleum extraction is essential to informing the best treatment and reuse options, which can potentially be optimized for a given geologic basin. Here, we used the US Geological Survey’s National Produced Waters Geochemical Database (PWGD) to determine if major ion chemistry could be used to classify accurately a produced water sample to a
Authors
Jenna L. Shelton, Aaron M. Jubb, Samuel Saxe, Emil D. Attanasi, Alexei Milkov, Mark A Engle, Philip A. Freeman, Christopher Shaffer, Madalyn S. Blondes