| snippet:
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Understanding the regional distribution of elements is important for optimizing mineral sampling programs at large and small scales. This study takes advantage of sediment samples from the National Uranium Resource Evaluation program and rock-chip samples from the National Geochemical Database to predict regional changes in relative abundance of elements in Wyoming. We use multiple imputation by chained equations to assign values to the incomplete geochemical data suite of these two historical programs, resulting in a complete geochemical dataset of 48 elements. From the complete dataset, we use hotspot analysis to predict areas of interest for both the sediment and rock data; areas are defined by watersheds. We then classify each watershed in the state by mineralization potential. The classification model is validated using rare earth elements and titanium as case studies using independent samples not included in the model. We find the model tends to overestimate the number of unsampled mineralized areas, which we interpret as more beneficial than underestimation. The model does well in predicting areas of known and potential mineralization, and provides a way to rapidly prioritize areas of interest and streamline sampling programs at the regional scale. |
| summary:
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Understanding the regional distribution of elements is important for optimizing mineral sampling programs at large and small scales. This study takes advantage of sediment samples from the National Uranium Resource Evaluation program and rock-chip samples from the National Geochemical Database to predict regional changes in relative abundance of elements in Wyoming. We use multiple imputation by chained equations to assign values to the incomplete geochemical data suite of these two historical programs, resulting in a complete geochemical dataset of 48 elements. From the complete dataset, we use hotspot analysis to predict areas of interest for both the sediment and rock data; areas are defined by watersheds. We then classify each watershed in the state by mineralization potential. The classification model is validated using rare earth elements and titanium as case studies using independent samples not included in the model. We find the model tends to overestimate the number of unsampled mineralized areas, which we interpret as more beneficial than underestimation. The model does well in predicting areas of known and potential mineralization, and provides a way to rapidly prioritize areas of interest and streamline sampling programs at the regional scale. |
| extent:
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[[-111.055709807891,40.994552622434],[-104.051902802091,45.0067634396875]] |
| accessInformation:
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Wyoming State Geological Survey |
| thumbnail:
|
thumbnail/thumbnail.png |
| maxScale:
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1.7976931348623157E308 |
| typeKeywords:
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["ArcGIS","ArcGIS Server","Data","Feature Access","Feature Service","providerSDS","Service"] |
| description:
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<div style='text-align:Left;'><div><div><p><span>This dataset accompanies WSGS Open File Report 2019-2 and includes imputed geochemical data for 48 elements from sediment samples from the National Uranium Resource Evaluation (NURE) program and rock-chip samples from the National Geochemical Database (NGBDR), as well as results of classification and hotspot analysis.</span></p></div></div></div> |
| licenseInfo:
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<div style='text-align:Left;'><div><div><p><span>USE CONSTRAINTS: Users of these data are cautioned against using the data at scales different from those at which the data were compiled. Using these data at a larger scale will not provide greater accuracy and is, in fact, a misuse of the data. --- The Wyoming State Geological Survey (WSGS) and State of Wyoming make no representation or warranty, expressed or implied, regarding the use, accuracy, or completeness of the data presented herein, or from a map derived from these data. The act of distribution shall not constitute such a warranty. The WSGS does not guarantee the digital data or any map derived from the data to be free of errors or inaccuracies. --- The WSGS and State of Wyoming disclaim any responsibility or liability for interpretations made from these digital data or from any map derived from these digital data, and for any decisions based on the digital data or derivative maps. The WSGS and State of Wyoming retain and do not waive sovereign immunity. --- We request that credit be expressly given to the “Wyoming State Geological Survey” when citing information from this publication.</span></p></div></div></div> |
| catalogPath:
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|
| title:
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WSGS_OFR_2019_02_LegacyRockAndSedimentData |
| type:
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Feature Service |
| url:
|
|
| tags:
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["minerals","samples","sediment","rock","classification","NURE","NGBDR","Wyoming"] |
| culture:
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en-US |
| portalUrl:
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|
| name:
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WSGS_OFR_2019_02_LegacyRockAndSedimentData |
| guid:
|
ED074EF3-76D1-429A-996A-D2C0DC57A565 |
| minScale:
|
0 |
| spatialReference:
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WGS_1984_Web_Mercator_Auxiliary_Sphere |