Multielement geochemical dataset of surficial materials for the northern Great Basin

Coombs, Mary Jane; Kotlyar, Boris B.; Ludington, Steve; Folger, Helen W.; Mossotti, Victor G.

doi:10.3133/ofr02227

“…The newer methods (Coombs et al, 2002) used to provide the data in our study are both more precise and more accurate than the first-generation NURE values. Analyses for this study are by inductively coupled plasma-atomic emission spectroscopy (ICPMS) after dissolution by acid and also by an organic extraction.…”

Section: Data Sourcesmentioning

confidence: 97%

“…As documented by Coombs et al (2002), the original data for this study area were obtained by the NURE program, using a wide variety of analytical methods on potentially disparate sample media, and results for individual elements are commonly not comparable from quadrangle to quadrangle. As an example, Figure 3 shows a map of arsenic distribution in the study area, based on the original NURE HSSR analyses.…”

Section: Data Sourcesmentioning

confidence: 99%

“…The data, along with sampling and analysis protocols, and some information on quality Smith (2001) and interpolated to a 2-km grid using inverse distance weighting methods. Contour intervals are the same as those used for arsenic in Coombs et al (2002) and in Figures 9 and 11. Note that the data for the Elko 1°× 2°quadrangle (easternmost) and the McDermitt quadrangle (northernmost) never exceed 15 ppm, whereas large areas of the other three quadrangles do.…”

Section: Data Sourcesmentioning

confidence: 99%

“…The locations of the samples and the study area are shown in Figure 1, and an index map showing place names mentioned in this report is shown in Figure 2. The samples were analyzed over a decade, beginning in 1992, and the data, along with some simple maps, are available as U.S. Geological Survey Open-File Report 02-0227 (Coombs et al, 2002). That report also contains detailed technical information about sample media type and size fraction, about the history of these data, and about their precision, accuracy, and quality control.…”

Section: Introductionmentioning

confidence: 99%

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Regional Surficial Geochemistry of the Northern Great Basin

Ludington¹

2006

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The regional distribution of arsenic and 20 other elements in stream-sediment samples in northern Nevada and southeastern Oregon was studied in order to gain new insights about the geologic framework and patterns of hydrothermal mineralization in the area. Data were used from 10,261 samples that were originally collected during the National Uranium Resource Evaluation (NURE) Hydrogeochemical and Stream Sediment Reconnaissance (HSSR) program in the 1970s. The data are available as U.S. Geological Survey Open-File Report 02-0227.The data were analyzed using traditional dot maps and interpolation between data points to construct highresolution raster images, which were correlated with geographic and geologic information using a geographic information system (GIS). Wavelength filters were also used to deconvolute the geochemical images into various textural components, in order to study features with dimensions of a few kilometers to dimensions of hundreds of kilometers.The distribution of arsenic, antimony, gold, and silver is different from distributions of the other elements in that they show a distinctive high background in the southeast part of the area, generally in areas underlain by the pre-Mesozoic craton. Arsenic is an extremely mobile element and can be used to delineate structures that served as conduits for the circulation of metal-bearing fluids. It was used to delineate large crustal structures and is particularly good for delineation of the Battle Mountain-Eureka mineral trend and the Steens lineament, which corresponds to a post-Miocene fault zone. Arsenic distribution patterns also delineated the Black Rock structural boundary, northwest of which the basement apparently consists entirely of Miocene and younger crust.Arsenic is also useful to locate district-sized hydrothermal systems and clusters of systems. Most important types of hydrothermal mineral deposit in the northern Great Basin appear to be strongly associated with arsenic; this is less so for low-sulfidation epithermal deposits.In addition to individual elements, the distribution of factor scores that resulted from principal component studies of the data was used. The strongest factor is characterized by Fe, Ti, V, Cu, Ni, and Zn and is used to map the distribution of distinctive basalts that are high in Cu, Ni, and Zn and that appear to be related to the Steens Basalt. The other important factor is related to hydrothermal precious metal mineralization and is characterized by Sb, Ag, As, Pb, Au, and Zn. The map of the distribution of this factor is similar in appearance to the one for arsenic, and we used wavelength filters to remove regional variations in the background for this factor score. The resulting residual map shows a very strong association with the most significant precious metal deposits and districts in the region. This residual map also shows a number of areas that are not associated with known mineral deposits, illustrating the utility of the method as a regional exploration tool. A number of these prospective are...

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“…Location map showing the area studied and the sample sites. The data are reported in Coombs et al (2002).…”

Section: Ludington Et Almentioning

confidence: 99%

Regional Surficial Geochemistry of the Northern Great Basin

Ludington

¹

,

Folger

²

,

Kotlyar

³

et al. 2006

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The regional distribution of arsenic and 20 other elements in stream-sediment samples in northern Nevada and southeastern Oregon was studied in order to gain new insights about the geologic framework and patterns of hydrothermal mineralization in the area. Data were used from 10,261 samples that were originally collected during the National Uranium Resource Evaluation (NURE) Hydrogeochemical and Stream Sediment Reconnaissance (HSSR) program in the 1970s. The data are available as U.S. Geological Survey Open-File Report 02-0227.The data were analyzed using traditional dot maps and interpolation between data points to construct highresolution raster images, which were correlated with geographic and geologic information using a geographic information system (GIS). Wavelength filters were also used to deconvolute the geochemical images into various textural components, in order to study features with dimensions of a few kilometers to dimensions of hundreds of kilometers.The distribution of arsenic, antimony, gold, and silver is different from distributions of the other elements in that they show a distinctive high background in the southeast part of the area, generally in areas underlain by the pre-Mesozoic craton. Arsenic is an extremely mobile element and can be used to delineate structures that served as conduits for the circulation of metal-bearing fluids. It was used to delineate large crustal structures and is particularly good for delineation of the Battle Mountain-Eureka mineral trend and the Steens lineament, which corresponds to a post-Miocene fault zone. Arsenic distribution patterns also delineated the Black Rock structural boundary, northwest of which the basement apparently consists entirely of Miocene and younger crust.Arsenic is also useful to locate district-sized hydrothermal systems and clusters of systems. Most important types of hydrothermal mineral deposit in the northern Great Basin appear to be strongly associated with arsenic; this is less so for low-sulfidation epithermal deposits.In addition to individual elements, the distribution of factor scores that resulted from principal component studies of the data was used. The strongest factor is characterized by Fe, Ti, V, Cu, Ni, and Zn and is used to map the distribution of distinctive basalts that are high in Cu, Ni, and Zn and that appear to be related to the Steens Basalt. The other important factor is related to hydrothermal precious metal mineralization and is characterized by Sb, Ag, As, Pb, Au, and Zn. The map of the distribution of this factor is similar in appearance to the one for arsenic, and we used wavelength filters to remove regional variations in the background for this factor score. The resulting residual map shows a very strong association with the most significant precious metal deposits and districts in the region. This residual map also shows a number of areas that are not associated with known mineral deposits, illustrating the utility of the method as a regional exploration tool. A number of these prospective are...

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Augmenting Stationary Covariance Functions with a Smoothness Hyperparameter and Improving Gaussian Process Regression Using a Structural Similarity Index

Chlingaryan,

Leung,

Melkumyan

2023

Math Geosci

0

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Gaussian process (GP) regression provides a probabilistic framework for modeling geochemistry in mineral resource estimation and environmental monitoring applications. An issue with this approach is that the kernel hyperparameters obtained by maximizing the log-marginal likelihood (LML) often produce GP posterior mean estimates that are overly smooth. This motivates the development of augmented kernels that are more capable of capturing the variability inherent in geological/geochemical processes than the existing stationary covariance functions like the Matérn kernels. This paper makes two contributions. First, it describes an extended class of stationary kernels that contain an extra smoothness hyperparameter ($$\alpha $$ α ) which can be learned from the input data. Valid intervals for $$\alpha $$ α that lead to positive semi-definiteness are determined. Second, it uses a statistical measure called the structural similarity index (SSIM) to quantify smoothness and the spatial fidelity of GP solutions with respect to the input samples. This provides a new way for validating and optimizing GP models. Statistical and spectral analyses provide insights into the behavior of $$\alpha $$ α in the augmented kernels which retain useful properties such as sparsity. Results on the Northern Great Basin geochemical dataset demonstrate that, all things being equal, (1) adjusting $$\alpha $$ α increases spatial fidelity in GP regression; (2) SSIM is a more reliable spatial quality measure than LML; and (3) the optimal $$\alpha $$ α value obtained is correlated with the Wiener entropy of the random process, which indicates the spectral flatness of the chemical signal in the Fourier domain. For GP regression, function smoothness may be defined using Sobolev space. The results show that $$\alpha $$ α regulates over-smoothing by moderating the rate of decay in the power spectrum of the equivalent kernel. Graphic abstract

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Multielement geochemical dataset of surficial materials for the northern Great Basin

Cited by 3 publications

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Regional Surficial Geochemistry of the Northern Great Basin

Regional Surficial Geochemistry of the Northern Great Basin

Regional Surficial Geochemistry of the Northern Great Basin

Augmenting Stationary Covariance Functions with a Smoothness Hyperparameter and Improving Gaussian Process Regression Using a Structural Similarity Index

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