Spatial databases of the Humboldt Basin mineral resource assessment, northern Nevada

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Title:

Spatial databases of the Humboldt Basin mineral resource assessment, northern Nevada

Abstract:

This metadata document describes the origin, generation, and format of the Pluton-Related Polymetallic tract map that accompanies the assessment of metallic mineral resources in the Humboldt River Basin (HRB). A "mineral-resource assessment tract" is a geographic region (a tract of land) that has been determined to possess geologic attributes that allow for the occurrence of mineral resources of a particular type(s). The assessment, finished in 2002, was carried out for all of northern Nevada, north of 38.5 degrees latitude, but focused primarily on the HRB. The assessment team delineated non-permissive, permissive, favorable, and prospective assessment tracts for Pluton-Related Polymetallic mineral occurrences or deposits using digital data and a combination of knowledge- and data-driven GIS-based analyses and modeling techniques (for more information about tract delineation and ranking, see the "Identification_Information / Description / Supplemental_Information" section of the metadata). Expert knowledge was used to (1) create, select, and appraise datasets for data-driven modeling, (2) delineate permissive and non-permissive assessment tracts, and (3) evaluate and revise preliminary mineral-resource assessment tracts derived from data-driven modeling. Data-driven modeling, including weights of evidence and weighted logistic regression, was used to delineate prospective and favorable assessment tracts. This land classification is stored in the tract attribute. Modeling was carried out with the ArcView GIS extension "Arc-SDM" (Spatial Data Modeller), developed by the U.S. Geological Survey and the Geological Survey of Canada (Kemp and others, 2001).

The mineral-resource assessment tract map is a GIS product, and is provided as an ESRI integer grid file named "plu" in an ESRI interchange-format file. This file can be viewed as an image or a raster with ESRI's Spatial Analyst extension. Both the expert and data-driven components of the assessment were conducted using data that range in scale from 1:250,000 to about 1:1,000,000. Manipulation and combination of these data has further decreased their collective resolution and accuracy to nearer 1:1,000,000. In practical terms, the ground resolution of the assessment tract map is about 2 km. The assessment tract map released here constitutes only part of the assessment, which additionally includes (1) new research and up-to-date reviews of the geology, mineral resources, and data for northern Nevada and (2) discussions on land classification and how to interpret and use the map.

For simplest use the grid should be symbolized with the tract attribute.

For addition information and details, see:

Wallace, A.R., Ludington, S., Mihalasky, M.J., Peters, S.G., Theodore, T.G., Ponce, D.A., John, D.A., and Berger, B.R., 2004, Assessment of metallic mineral resources in the Humboldt River Basin, Northern Nevada, with a section on PGE potential of the Humboldt mafic complex by M.L. Zientek, G.B. Sidder, and R.A. Zierenberg: U.S. Geological Survey Bulletin B-2218, CD-ROM.

Kemp, L.D., Bonham-Carter, G.F., Raines, G.L. and Looney, C.G., 2001, Arc-SDM: Arcview extension for spatial data modelling using weights of evidence, logistic regression, fuzzy logic and neural network analysis, [<http://www.ige.unicamp.br/sdm/>]>.

Supplemental_Information:

Note: The metadata section, Quantitative_Attribute_Accuracy_Assessment, is truncated in the FAQ version of the metadata, if you are interested in a more detailed discussion of the quantitative assessment of error and uncertainty related to the WofE and WLR analysis and associated tables (WOE, WOEVAR, PBR, LRCOEF) refer to the longer form of the FGDC metadata. The following is an overview of the analysis and modeling methods used to generate the HRB mineral-resource assessment tract maps.

The tract map is a GIS-based product, and was generated by integrating multiple geoscientific maps using weights of evidence (WofE) and weighted logistic regression (WLR) mineral potential modeling techniques. Modeling was carried out with the ArcView GIS extension "Arc-SDM" (Spatial Data Modeller), developed by the U.S. Geological Survey and the Geological Survey of Canada (<http://www.ige.unicamp.br/sdm/>).

The HRB mineral-resource assessment team delineated non-permissive, permissive, favorable, and prospective assessment tracts using digital data and a combination of knowledge- and data-driven GIS-based analyses and modeling techniques. This land classification is stored in the tract attribute. Expert knowledge was used to (1) create, select, and appraise datasets for data-driven modeling, (2) delineate permissive and non-permissive assessment tracts, and (3) evaluate and revise preliminary mineral-resource assessment maps derived from data-driven modeling. Data-driven modeling, including weights of evidence (WofE) and weighted logistic regression (WLR), was used to delineate prospective and favorable assessment tracts.

WofE and WLR are empirical, data-driven methodologies for integrating spatial data patterns and building predictive models (Bonham-Carter, 1994). They use (1) conditional probabilities to measure the spatial association between point objects and patterns, and (2) Bayes' probability theorem (WofE) or WLR to statistically integrate the patterns to predict the distribution of the point objects. As applied in this mineral-resource assessment, the patterns represent geoscientific phenomena that are considered useful mineral predictors, and are referred to as "evidence maps". The point objects represent known mineral sites, and are referred to as "training sites". Evidence and training datasets used to generate the Pluton-Related Polymetallic mineral-resource assessment tract map are described in Wallace and others (2004).

Evidence maps are typically multi-class and include representations of geological map units, structure, and geochemical and geophysical anomalies (as well as remotely sensed images and other earth-observation data, and even conceptual or interpretive maps). In order to facilitate combination, the evidence maps are usually reduced to predictor patterns of a few discrete states, typically binary- or ternary-class, where the spatial association between the training sites and an evidence map is optimized. The evidence maps collectively constitute "layers of evidence".

Training sites are used to identify and weight the importance of predictor patterns on evidence maps. Training sites collectively possess characteristics that are common to a particular deposit type. It is presumed that their location and presence enable prediction of the particular deposit type represented. Training sites are regarded as binary, either present or absent.

WofE and WLR models consist of integrated predictor patterns and are expressed in the form of a single "favorability map" of posterior probability. The favorability map represents the spatial distribution of training sites in terms of the spatial distribution of predictor patterns, as well as the predicted distribution of yet unidentified sites. The favorability map is ranked relatively from lowest to highest as "non-permissive", "permissive", "favorable", and "prospective" mineral-resource assessment tracts. The non-permissive and permissive mineral-resource assessment tracts were delineated using a previous, knowledge-driven assessment for Nevada (Cox and others, 1996) because the assessment team felt this provided the best definition of these tracts. Prospective and favorable tracts reflect the combination of the evidence maps. For a given combination, the contribution of each evidence map to the level of favorability is derived statistically from the spatial association between the distribution pattern of the training sites and the geoscientific phenomena represented in the evidence maps. For example, if the statistical calculations determine that training sites have a greater spatial association with geochemical anomalies than with a geophysical anomalies, then the geochemical anomalies contribute more to the level of favorability than do the geophysical anomalies. The implication is that certain evidence map combinations represent a greater likelihood that mineralizing processes took place in a given area than other combinations. Thus, a prospective area represents the optimum combination of the evidence maps, whereas a favorable area consists of a somewhat less optimum, but still relatively significant, combination. For additional information about ranks, See the "Identification_Information / Use_Constraints" and "Entity_and_Attribute_Information / Detailed_Description / Attribute / Attribute_Label / TRACT" sections of the metadata, as well as Chapter 2 of Wallace and others (2004).

For this assessment, WofE was used to analyze the bivariate spatial associations between the training sites and the various evidence maps, and thus to define the predictor patterns. WLR was used to integrate (combine) the evidence maps and delineate the prospective and favorable assessment tracts. In some cases, the evidence maps selected by the assessment team had a high conditional dependence (mutually interrelated). By using WLR to combine the maps, bias caused by conditional dependency was avoided. The non-permissive-permissive tract boundary was delineated using a previous, knowledge-driven assessment for Nevada (Cox and others, 1996) because the assessment team felt this provided the best definition of these tracts.

Bonham-Carter, G.F., 1994, Geographic Information Systems for Geoscientists: Modelling with GIS (Computer Methods in the Geosciences Volume 13): Tarrytown, New York, Pergamon Press/Elsevier Science Publications, 398 p.

Singer, D.A., ed., 1996, An analysis of Nevada's metal-bearing mineral resources: Nevada Bureau of Mines and Geology Open-file Report 96-2, <http://www.nbmg.unr.edu/dox/ofr962/cover.pdf>.

For addition information and details, see:

Wallace, A.R., Ludington, S., Mihalasky, M.J., Peters, S.G., Theodore, T.G., Ponce, D.A., John, D.A., and Berger, B.R., 2004, Assessment of metallic mineral resources in the Humboldt River Basin, Northern Nevada, with a section on PGE potential of the Humboldt mafic complex by M.L. Zientek, G.B. Sidder, and R.A. Zierenberg: U.S. Geological Survey Bulletin B-2218, CD-ROM.

  1. How should this data set be cited?

Mihalasky, Mark J. , and Moyer, Lorre A. , 2004, Spatial databases of the Humboldt Basin mineral resource assessment, northern Nevada: U. S. Geological Survey Open-File Report 2004-1245, U. S. Geological Survey, Menlo Park, CA.

Online Links:

Other_Citation_Details: None

  1. What geographic area does the data set cover?

West_Bounding_Coordinate: -120.00

East_Bounding_Coordinate: -114.05

North_Bounding_Coordinate: 42.00

South_Bounding_Coordinate: 38.50

  1. What does it look like?
  2. Does the data set describe conditions during a particular time period?

Calendar_Date: 2004

Currentness_Reference:

Published 2004. Mineral-resource assessment tract grid created in 2001.

  1. What is the general form of this data set?

Geospatial_Data_Presentation_Form: ESRI integer raster digital data

  1. How does the data set represent geographic features?

a.       How are geographic features stored in the data set?

Indirect_Spatial_Reference: none

This is a Raster data set. It contains the following raster data types:

      • Dimensions 3900 x 5142 x 1, type Grid Cell

b.      What coordinate system is used to represent geographic features?

The map projection used is Lambert Conformal Conic.

Projection parameters:

Standard_Parallel: 33.0

Standard_Parallel: 45.0

Longitude_of_Central_Meridian: -117.0

Latitude_of_Projection_Origin: 0.0

False_Easting: 0.0

False_Northing: 0.0

Planar coordinates are encoded using row and column
Abscissae (x-coordinates) are specified to the nearest 100.000000
Ordinates (y-coordinates) are specified to the nearest 100.000000
Planar coordinates are specified in meters

The horizontal datum used is North American Datum 1927.
The ellipsoid used is Clarke 1866.
The semi-major axis of the ellipsoid used is 6,378,206.4.
The flattening of the ellipsoid used is 1/294.98.

  1. How does the data set describe geographic features?

plu

Pluton-Related Metallic Mineral-Resource Assessment Tract Map - ESRI value attribute table. The first two fields of this table consist of value and count, which are standard ESRI grid attributes. The remaining fields were appended to the value attribute table and contain (1) the presence or absence of a given predictor pattern (all fields taken collectively represent a specific unique overlap condition among the predictor patterns), (2) WofE and WLR statistics for each unique overlap condition (which is appended from the file wofe.dbf, a table generated by Arc-Sdm), and (3) the categorical mineral-resource assessment tract rank, which is detailed in the attribute "Tract". (Source: Mihalasky, M.J.,and Wallace, A.R., 2004, CHAPTER 2. Assessment Concepts and Methodology, in, Wallace, A.R., Ludington, S., Mihalasky, M.J., Peters, S.G., Theodore, T.G., Ponce, D.A., John, D.A., and Berger, B.R., in review, Assessment of metallic mineral resources in the Humboldt River Basin, Northern Nevada, with a section on PGE potential of the Humboldt mafic complex by M.L. Zientek, G.B. Sidder, and R.A. Zierenberg: U.S. Geological Survey Bulletin B-2218, CD-ROM.)

VALUE

Object ID. The internal feature number, which is part of the value attribute table (VAT) containing attributes for a ESRI integer raster. (Source: <http://www.esri.com/library/glossary/t_z.html#V>)

Range of values

Minimum:

0

Maximum:

29

Units:

none

Resolution:

1

COUNT

The count of grid cells with a particular value. (Source: <http://www.esri.com/library/glossary/t_z.html#V>)

Range of values

Minimum:

12

Maximum:

11184307

Units:

none

Resolution:

1

ASFREQ

Presence or absence of predictor pattern asfreq - Arsenic anomaly, As-frequency, representing NURE As concentration (partial digestion) that was processed in the frequency domain. (Source: Mihalasky, M.J.,and Wallace, A.R., 2004, CHAPTER 2. Assessment Concepts and Methodology, in, Wallace, A.R., Ludington, S., Mihalasky, M.J., Peters, S.G., Theodore, T.G., Ponce, D.A., John, D.A., and Berger, B.R., 2004, Assessment of metallic mineral resources in the Humboldt River Basin, Northern Nevada, with a section on PGE potential of the Humboldt mafic complex by M.L. Zientek, G.B. Sidder, and R.A. Zierenberg: U.S. Geological Survey Bulletin B-2218, CD-ROM.)

Value

Definition

0

predictor pattern absent

1

predictor pattern present

CU_PB_ZN

Presence or absence of predictor pattern cu_pb_zn - Cu-Pb-Zn anomaly, representing a combined signature calculated from NURE Cu, Pb, and Zn concentrations (total digestion). (Source: Mihalasky, M.J.,and Wallace, A.R., 2004, CHAPTER 2. Assessment Concepts and Methodology, in, Wallace, A.R., Ludington, S., Mihalasky, M.J., Peters, S.G., Theodore, T.G., Ponce, D.A., John, D.A., and Berger, B.R., 2004, Assessment of metallic mineral resources in the Humboldt River Basin, Northern Nevada, with a section on PGE potential of the Humboldt mafic complex by M.L. Zientek, G.B. Sidder, and R.A. Zierenberg: U.S. Geological Survey Bulletin B-2218, CD-ROM.)

Value

Definition

0

predictor pattern absent

1

predictor pattern present

GRVBSHI

Presence or absence of predictor pattern grvbshi - Basement gravity terranes, reflecting regions of similar anomaly features or geophysical fabric. (Source: Mihalasky, M.J.,and Wallace, A.R., 2004, CHAPTER 2. Assessment Concepts and Methodology, in, Wallace, A.R., Ludington, S., Mihalasky, M.J., Peters, S.G., Theodore, T.G., Ponce, D.A., John, D.A., and Berger, B.R., 2004, Assessment of metallic mineral resources in the Humboldt River Basin, Northern Nevada, with a section on PGE potential of the Humboldt mafic complex by M.L. Zientek, G.B. Sidder, and R.A. Zierenberg: U.S. Geological Survey Bulletin B-2218, CD-ROM.)

Value

Definition

0

predictor pattern absent

1

predictor pattern present

GVBSLF

Presence or absence of predictor pattern gvbslf - Basement gravity lineaments, reflecting abrupt lateral variations in the density of basement rocks. (Source: Mihalasky, M.J.,and Wallace, A.R., 2004, CHAPTER 2. Assessment Concepts and Methodology, in, Wallace, A.R., Ludington, S., Mihalasky, M.J., Peters, S.G., Theodore, T.G., Ponce, D.A., John, D.A., and Berger, B.R., 2004, Assessment of metallic mineral resources in the Humboldt River Basin, Northern Nevada, with a section on PGE potential of the Humboldt mafic complex by M.L. Zientek, G.B. Sidder, and R.A. Zierenberg: U.S. Geological Survey Bulletin B-2218, CD-ROM.)

Value

Definition

0

predictor pattern absent

1

predictor pattern present

LITHODVR

Presence or absence of predictor pattern lithodvr - Lithodiversity of the geologic map of Nevada. (Source: Mihalasky, M.J.,and Wallace, A.R., 2004, CHAPTER 2. Assessment Concepts and Methodology, in, Wallace, A.R., Ludington, S., Mihalasky, M.J., Peters, S.G., Theodore, T.G., Ponce, D.A., John, D.A., and Berger, B.R., 2004, Assessment of metallic mineral resources in the Humboldt River Basin, Northern Nevada, with a section on PGE potential of the Humboldt mafic complex by M.L. Zientek, G.B. Sidder, and R.A. Zierenberg: U.S. Geological Survey Bulletin B-2218, CD-ROM.)

Value

Definition

0

predictor pattern absent

1

predictor pattern present

PLUTONS

Presence or absence of predictor pattern plutons - Proximity to plutonic rocks represented on the geologic map of Nevada, including Tri, Tmi, Ti, Tr2, Tr1, TJgr, Tgr, Mzgr, Kgr, KJd, Jgr, TRgr, and TRlgr (ranging in age from Middle-Late Triassic to late Miocene, but predominantly are Mesozoic). (Source: Mihalasky, M.J.,and Wallace, A.R., 2004, CHAPTER 2. Assessment Concepts and Methodology, in, Wallace, A.R., Ludington, S., Mihalasky, M.J., Peters, S.G., Theodore, T.G., Ponce, D.A., John, D.A., and Berger, B.R., 2004, Assessment of metallic mineral resources in the Humboldt River Basin, Northern Nevada, with a section on PGE potential of the Humboldt mafic complex by M.L. Zientek, G.B. Sidder, and R.A. Zierenberg: U.S. Geological Survey Bulletin B-2218, CD-ROM.)

Value

Definition

0

predictor pattern absent

1

predictor pattern present

SKARNS

Presence or absence of predictor pattern skarns - Proximity to MRDS skarn deposits, including Cox and Singer (1986) deposit model types 14a, 18a, 18b, 18c, 18d, 18e, and 18f. (Source: Mihalasky, M.J.,and Wallace, A.R., 2004, CHAPTER 2. Assessment Concepts and Methodology, in, Wallace, A.R., Ludington, S., Mihalasky, M.J., Peters, S.G., Theodore, T.G., Ponce, D.A., John, D.A., and Berger, B.R., 2004, Assessment of metallic mineral resources in the Humboldt River Basin, Northern Nevada, with a section on PGE potential of the Humboldt mafic complex by M.L. Zientek, G.B. Sidder, and R.A. Zierenberg: U.S. Geological Survey Bulletin B-2218, CD-ROM.)

Value

Definition

0

predictor pattern absent

1

predictor pattern present

AREA_SQM

Area in square meters of the unique condition overlap, calculated by Arc-SDM. (Source: Kemp, L.D., Bonham-Carter, G.F., Raines, G.L. and Looney, C.G., 2001, Arc-SDM: Arcview extension for spatial data modelling using weights of evidence, logistic regression, fuzzy logic and neural network analysis, [<http://www.ige.unicamp.br/sdm/]>.)

Range of values

Minimum:

0.00

Maximum:

42654910000.00

Units:

meters square

Resolution:

1

TRNGPOINTS

Number of training sites that fall within the unique condition overlap. (Source: Kemp, L.D., Bonham-Carter, G.F., Raines, G.L. and Looney, C.G., 2001, Arc-SDM: Arcview extension for spatial data modelling using weights of evidence, logistic regression, fuzzy logic and neural network analysis, [<http://www.ige.unicamp.br/sdm/]>.)

Range of values

Minimum:

0

Maximum:

12

Units:

count

Resolution:

1

POST_PROB

Posterior probability. The probability that a unit cell (1 km for this study) contains a training site. Due to conditional dependency problems, this probability it highly inflated. (Source: Kemp, L.D., Bonham-Carter, G.F., Raines, G.L. and Looney, C.G., 2001, Arc-SDM: Arcview extension for spatial data modelling using weights of evidence, logistic regression, fuzzy logic and neural network analysis, [<http://www.ige.unicamp.br/sdm/]>.)

Range of values

Minimum:

0.00000000

Maximum:

0.98268820

Units:

Probability [0,1]

PSTPRBNRM

Normalized posterior probability. The posterior probability re-scaled so that the overall measure of conditional independence is satisfied. Re-scaling is done by multiplying by Training Site / Sum of (area * probaiblity), where the summation is over all unique conditions. (Source: Kemp, L.D., Bonham-Carter, G.F., Raines, G.L. and Looney, C.G., 2001, Arc-SDM: Arcview extension for spatial data modelling using weights of evidence, logistic regression, fuzzy logic and neural network analysis, [<http://www.ige.unicamp.br/sdm/]>.)

Range of values

Minimum:

0.00000000

Maximum:

0.47208347

Units:

Probability [0,1]

POST_LOGIT

Posterior logit. The sum of weights from each evidence map added to the prior logit. (Source: Kemp, L.D., Bonham-Carter, G.F., Raines, G.L. and Looney, C.G., 2001, Arc-SDM: Arcview extension for spatial data modelling using weights of evidence, logistic regression, fuzzy logic and neural network analysis, [<http://www.ige.unicamp.br/sdm/]>.)

Range of values

Minimum:

-11.32379622

Maximum:

4.03890378

Units:

Logits

SUM_WEIGHT

The sum of the weights (W+ and W-) for each evidence map. (Source: Kemp, L.D., Bonham-Carter, G.F., Raines, G.L. and Looney, C.G., 2001, Arc-SDM: Arcview extension for spatial data modelling using weights of evidence, logistic regression, fuzzy logic and neural network analysis, [<http://www.ige.unicamp.br/sdm/]>.)

Range of values

Minimum:

-3.67370000

Maximum:

11.68900000

Units:

none

UNCERTAINT

Uncertainty. The standard deviation due to the calculation of weights. (Source: Kemp, L.D., Bonham-Carter, G.F., Raines, G.L. and Looney, C.G., 2001, Arc-SDM: Arcview extension for spatial data modelling using weights of evidence, logistic regression, fuzzy logic and neural network analysis, [<http://www.ige.unicamp.br/sdm/]>.)

Range of values

Minimum:

0.00000000

Maximum:

0.10609968

Units:

none

MSNG_DATA

Missing data. The standard deviation due to missing data. (Source: Kemp, L.D., Bonham-Carter, G.F., Raines, G.L. and Looney, C.G., 2001, Arc-SDM: Arcview extension for spatial data modelling using weights of evidence, logistic regression, fuzzy logic and neural network analysis, [<http://www.ige.unicamp.br/sdm/]>.)

Range of values

Minimum:

0.00000000

Maximum:

0.00000000

Units:

none

TOT_UNCRTY

Total uncertainty. The combined standard deviation due to weights and missing data. (Source: Kemp, L.D., Bonham-Carter, G.F., Raines, G.L. and Looney, C.G., 2001, Arc-SDM: Arcview extension for spatial data modelling using weights of evidence, logistic regression, fuzzy logic and neural network analysis, [<http://www.ige.unicamp.br/sdm/]>.)

Range of values

Minimum:

0.00000000

Maximum:

0.10609968

Units:

none

LRPOSTPROB

Posterior probability calculated using logistic regression. (Source: Kemp, L.D., Bonham-Carter, G.F., Raines, G.L. and Looney, C.G., 2001, Arc-SDM: Arcview extension for spatial data modelling using weights of evidence, logistic regression, fuzzy logic and neural network analysis, [<http://www.ige.unicamp.br/sdm/]>.)

Range of values

Minimum:

0.00000000

Maximum:

0.43087000

Units:

Probability [0,1]

LRTVALUE

Weighted logistic regression posterior probability Studentized-T value. This is the WLR posterior probability divided by its standard deviation. (Source: Kemp, L.D., Bonham-Carter, G.F., Raines, G.L. and Looney, C.G., 2001, Arc-SDM: Arcview extension for spatial data modelling using weights of evidence, logistic regression, fuzzy logic and neural network analysis, [<http://www.ige.unicamp.br/sdm/]>.)

Range of values

Minimum:

0.00000000

Maximum:

6.22680000

Units:

none

LR_STD_DEV

Logistic regression posterior probability standard deviation. (Source: Kemp, L.D., Bonham-Carter, G.F., Raines, G.L. and Looney, C.G., 2001, Arc-SDM: Arcview extension for spatial data modelling using weights of evidence, logistic regression, fuzzy logic and neural network analysis, [<http://www.ige.unicamp.br/sdm/]>.)

Range of values

Minimum:

0.00000000

Maximum:

0.07721000

Units:

none

TRACT

Mineral-resource assessment tract. Descriptive class name assigned to the WLR posterior probability by the assessment team. (Source: Mihalasky, M.J.,and Wallace, A.R., 2004, CHAPTER 2. Assessment Concepts and Methodology, in, Wallace, A.R., Ludington, S., Mihalasky, M.J., Peters, S.G., Theodore, T.G., Ponce, D.A., John, D.A., and Berger, B.R., 2004, Assessment of metallic mineral resources in the Humboldt River Basin, Northern Nevada, with a section on PGE potential of the Humboldt mafic complex by M.L. Zientek, G.B. Sidder, and R.A. Zierenberg: U.S. Geological Survey Bulletin B-2218, CD-ROM.)

Value

Definition

Non-Permissive

Non-permissive tracts are those areas judged to have a negligible probability of containing a mineral deposit or occurrence, or that are covered by more than 1 km of Cenozoic rocks or alluvial sediments. As described by Singer (1993), these areas have roughly less than a 1 in 100,000 to 1,000,000 chance of containing undiscovered deposits of the type being assessed. The non-permissive designation is based on absence of geologic environments and (or) known mineralizing processes that are understood to be necessary for formation of the type of mineral occurrence or deposit under consideration. Non-permissive tracts delineated in the HRB mineral-resource assessment are similar to those used and defined in the Nevada assessment (Singer, 1996), differing only in the depth-to-basement maps used to define areas of thick Cenozoic volcanic or sedimentary deposits.

Note: In the attribute table associated with this ESRI grid, non-permissive mineral-resource assessment tracts have blank attributes, as these tracts were delineated by knowledge-driven means, not by data-driven WofE and WLR analysis and modeling techniques.

For additional information, see Wallace and others (2004).

Singer, D.A., 1993, Basic concepts in the three-part quantitative assessments of undiscovered mineral resources: Nonrenewable Resources, v. 2, p. 69-81.

Singer, D.A., ed., 1996, An analysis of Nevada's metal-bearing mineral resources: Nevada Bureau of Mines and Geology Open-file Report 96-2, <http://www.nbmg.unr.edu/dox/ofr962/cover.pdf>.

Wallace, A.R., Ludington, S., Mihalasky, M.J., Peters, S.G., Theodore, T.G., Ponce, D.A., John, D.A., and Berger, B.R., 2004, Assessment of metallic mineral resources in the Humboldt River Basin, Northern Nevada, with a section on PGE potential of the Humboldt mafic complex by M.L. Zientek, G.B. Sidder, and R.A. Zierenberg: U.S. Geological Survey Bulletin B-2218, CD-ROM.

Permissive

Permissive areas, approximately corresponding to a rank level of "low favorability" for undiscovered deposits. Permissive tracts delineated in the HRB mineral-resource assessment are similar to those used and defined in the Nevada assessment (Singer, 1996), differing only in the depth-to-basement maps used to define areas of thick Cenozoic volcanic or sedimentary deposits. Permissive tracts are regions that might contain a mineralized system within a depth of 1 km beneath the surface. These tracts may or may not contain mineral deposits or occurrences, and their designation as permissive does not necessarily imply that any resources, if they are present, will be discovered. This designation is based on the presence of one or more geologic factors that the assessment team considered to be important, some of which may be widespread, and that are known to have been involved with the formation of mineral deposits and occurrences elsewhere in the assessment area. By definition, permissive tracts include favorable and prospective areas and thus are considered to contain virtually all undiscovered deposits of a certain type or group.

Note: In the attribute table associated with the ESRI grid, permissive mineral-resource assessment tracts that have blank attributes are outside the extent of geochemistry evidence map coverage and were delineated by knowledge-driven means. Tracts that have values are within the extent of geochemistry evidence map coverage, where data-driven WofE and WLR analysis and modeling was carried out to delineate the favorable and prospective tracts.

For additional information, see Wallace and others (2004).

Singer, D.A., ed., 1996, An analysis of Nevada's metal-bearing mineral resources: Nevada Bureau of Mines and Geology Open-file Report 96-2, <http://www.nbmg.unr.edu/dox/ofr962/cover.pdf>.

Wallace, A.R., Ludington, S., Mihalasky, M.J., Peters, S.G., Theodore, T.G., Ponce, D.A., John, D.A., and Berger, B.R., 2004, Assessment of metallic mineral resources in the Humboldt River Basin, Northern Nevada, with a section on PGE potential of the Humboldt mafic complex by M.L. Zientek, G.B. Sidder, and R.A. Zierenberg: U.S. Geological Survey Bulletin B-2218, CD-ROM.

Favorable

Favorable areas, approximately corresponding to a rank level of "moderate favorability" for undiscovered deposits. The favorable mineral-resource assessment tract was generated using WofE and WLR analysis and modeling techniques, but limited in extent to areas where NURE geochemistry evidence maps were available (this area covers most of northern Nevada, except for the southern-most, southwestern, and eastern-most extent of the study area; for additional details, see Wallace, 2004, Chapters 2 and 5). The HRB assessment team created and (or) selected datasets for mineral-resource analysis and modeling that represent a number of important regional processes believed to be related to formation of mineral deposits and occurrences. The relative rankings of the tracts (permissive, favorable, and prospective) reflect the combination of these datasets for each type of mineralizing system assessed. For a given combination, the contribution of each evidence map to the level of favorability is derived mathematically from the spatial association between the distribution pattern of the known mineral occurrences and deposits and the geoscientific phenomena represented in the maps. For example, if the mathematical calculations determine that mineral occurrences and deposits have a greater spatial association with geochemical anomalies than with a geophysical anomalies, then the geochemical anomalies contribute more to the level of favorability than do the geophysical anomalies. The implication is that certain evidence map combinations represent a greater likelihood that mineralizing processes took place in a given area than other combinations. Thus, a prospective area (tract) represents the optimum combination of the evidence maps, whereas a favorable area (tract) consists of a somewhat less optimum, but still relatively significant, combination. Combining the evidence maps and determining the threshold between prospective and favorable also is done mathematically. For the pluton-related polymetallic assessment tract map, the favorable-prospective rank boundary is defined by plotting WLR favorability against cumulative assessment area and identifying the most prominent break-point in the curve above the prior favorability (prior probability = 0.0005; favorable-prospective posterior probability boundary = 0.00076; for details, see Wallace and others, 2004, Chapters 2 and 7). The shape and distribution of the prospective and favorable tracts is determined by the overlap intersections among the patterns of geoscientific phenomena represented in each of the evidence maps.

Wallace, A.R., Ludington, S., Mihalasky, M.J., Peters, S.G., Theodore, T.G., Ponce, D.A., John, D.A., and Berger, B.R., 2004, Assessment of metallic mineral resources in the Humboldt River Basin, Northern Nevada, with a section on PGE potential of the Humboldt mafic complex by M.L. Zientek, G.B. Sidder, and R.A. Zierenberg: U.S. Geological Survey Bulletin B-2218, CD-ROM.

Prospective

Prospective areas, approximately corresponding to a rank level of "high favorability" for undiscovered deposits. The prospective mineral-resource assessment tract was generated using WofE and WLR analysis and modeling techniques, but limited in extent to areas where NURE geochemistry evidence maps were available (this area covers most of northern Nevada, except for the southern-most, southwestern, and eastern-most extent of the study area; for additional details, see Wallace, 2004, Chapters 2 and 5). The HRB assessment team created and (or) selected datasets for mineral-resource analysis and modeling that represent a number of important regional processes believed to be related to formation of mineral deposits and occurrences. The relative rankings of the tracts (permissive, favorable, and prospective) reflect the combination of these datasets for each type of mineralizing system assessed. For a given combination, the contribution of each evidence map to the level of favorability is derived mathematically from the spatial association between the distribution pattern of the known mineral occurrences and deposits and the geoscientific phenomena represented in the evidence maps. For example, if the mathematical calculations determine that mineral occurrences and deposits have a greater spatial association with geochemical anomalies than with a geophysical anomalies, then the geochemical anomalies contribute more to the level of favorability than do the geophysical anomalies. The implication is that certain evidence map combinations represent a greater likelihood that mineralizing processes took place in a given area than other combinations. Thus, a prospective area (tract) represents the optimum combination of the evidence maps, whereas a favorable area (tract) consists of a somewhat less optimum, but still relatively significant, combination. Combining the evidence maps and determining the threshold between prospective and favorable also is done mathematically. For the pluton-related polymetallic assessment tract map, the favorable-prospective rank boundary is defined by plotting WLR favorability against cumulative assessment area and identifying the most prominent break-point in the curve above the prior favorability (prior probability = 0.0005; favorable-prospective posterior probability boundary = 0.00076; for details, see Wallace and others, 2004, Chapters 2 and 7). The shape and distribution of the prospective and favorable tracts is determined by the overlap intersections among the patterns of geoscientific phenomena represented in each of the evidence maps.

Wallace, A.R., Ludington, S., Mihalasky, M.J., Peters, S.G., Theodore, T.G., Ponce, D.A., John, D.A., and Berger, B.R., 2004, Assessment of metallic mineral resources in the Humboldt River Basin, Northern Nevada, with a section on PGE potential of the Humboldt mafic complex by M.L. Zientek, G.B. Sidder, and R.A. Zierenberg: U.S. Geological Survey Bulletin B-2218, CD-ROM.

Entity_and_Attribute_Overview:

The following is a list of the evidence maps used to create the pluton-related polymetallic favorable and prospective mineral-resource assessment tracts:

asfreq - Arsenic anomaly, As-frequency, representing NURE As concentration (partial digestion) that was processed in the frequency domain.

cu_pb_zn - Cu-Pb-Zn anomaly, representing a combined signature calculated from NURE Cu, Pb, and Zn concentrations (total digestion).

grvbshi - Basement gravity terranes, reflecting regions of similar anomaly features or geophysical fabric.

gvbslf - Basement gravity lineaments, reflecting abrupt lateral variations in the density of basement rocks.

lithodvr - Lithodiversity of the geologic map of Nevada.

plutons - Proximity to plutonic rocks represented on the geologic map of Nevada, including Tri, Tmi, Ti, Tr2, Tr1, TJgr, Tgr, Mzgr, Kgr, KJd, Jgr, TRgr, and TRlgr (ranging in age from Middle-Late Triassic to late Miocene, but predominantly are Mesozoic).

skarns - Proximity to MRDS skarn deposits, including Cox and Singer (1986) deposit model types 14a, 18a, 18b, 18c, 18d, 18e, and 18f.

Entity_and_Attribute_Detail_Citation:

For addition information on the evidence maps listed in "Entity_and_Attribute_Overview", see the "Lineage / Source_Information / Source_Contribution" section of the metadata above.

Source data and processing of the datasets used for modeling are detailed in:

Wallace, A.R., Ludington, S., Mihalasky, M.J., Peters, S.G., Theodore, T.G., Ponce, D.A., John, D.A., and Berger, B.R., 2004, Assessment of metallic mineral resources in the Humboldt River Basin, Northern Nevada, with a section on PGE potential of the Humboldt mafic complex by M.L. Zientek, G.B. Sidder, and R.A. Zierenberg: U.S. Geological Survey Bulletin B-2218, CD-ROM.


Who produced the data set?

  1. Who are the originators of the data set? (may include formal authors, digital compilers, and editors)
    • Mark J. Mihalasky
    • Lorre A. Moyer
  2. Who also contributed to the data set?

Mark J. Mihalasky and the Humboldt Mineral-Resource Assessment Team, Western Region Mineral Resources Team, U. S. Geological Survey. For details, see Wallace and others (2004, Chapter 2).

Wallace, A.R., Ludington, S., Mihalasky, M.J., Peters, S.G., Theodore, T.G., Ponce, D.A., John, D.A., and Berger, B.R., 2004, Assessment of metallic mineral resources in the Humboldt River Basin, Northern Nevada, with a section on PGE potential of the Humboldt mafic complex by M.L. Zientek, G.B. Sidder, and R.A. Zierenberg: U.S. Geological Survey Bulletin B-2218, CD-ROM.

  1. To whom should users address questions about the data?

Alan R. Wallace
U. S. Geological Survey
Research Geologist
C/O Mackay School of Mines
Reno, NV 89557
USA

1-775-784-5789 (voice)
1-775-784-5079 (FAX)
alan@usgs.gov

Contact_Instructions:

The originator of the dataset, Mark Mihalasky, is no longer with U. S. Geological Survey. Contact Alan Wallace (alan@usgs.gov) or Mark Mihalasky at mihalasky@hotmail.com.


Why was the data set created?

The Humboldt River Basin (HRB) is an arid to semiarid, internally drained basin that covers approximately 43,000 km2 in northern Nevada. The basin contains a wide variety of metallic and non-metallic mineral deposits and occurrences. In 1992, and again in 1996, the Nevada State Office of the Bureau of Land Management (BLM) requested a mineral-resource assessment of the HRB to aid their land-use planning. The purpose of the assessment was to (1) assess the favorability for undiscovered metallic mineral occurrences and deposits in the HRB and adjacent areas, (2) provide an analysis of the mineral-resource favorability that can be reproduced on the basis of the data and defined assumptions, and (3) present that assessment in a digital format, using a Geographic Information System (GIS). Finished in 2002, the assessment includes three GIS mineral-resource assessment tract maps (see below). The tract map released here constitutes only part of the assessment, which additionally includes (1) new research and up-to-date reviews of the geology, mineral resources, and data for northern Nevada and (2) discussions on land classification and how to interpret and use the assessment tract map.

The HRB mineral-resource study assessed the potential for undiscovered mineralizing systems (pluton-related polymetallic, sedimentary rock-hosted Au-Ag, and epithermal Au-Ag) and contained mineral deposits and occurrences, instead of the specific deposit types related to those systems. The rationale for this approach was that (1) mineralizing systems are larger than individual mineral deposits, (2) mineralizing systems can form more than one individual deposit type, and (3) the presence of one mineral deposit type might indicate the presence of a larger system. In some locations, the various deposit types in a mineralizing system represent a continuum of site-specific processes of mineral deposition. As a result, the economic viability of any part(s) of the mineralizing system is a function of its metal endowment. Thus, the approach that was taken in the HRB assessment addresses areas where mineralizing processes took place over relatively large areas to form concentrations of metallic minerals.

Three fundamental types of mineralizing systems are addressed in the HRB mineral-resource assessment: (1) pluton-related polymetallic, (2) sedimentary rock-hosted Au-Ag, and (3) epithermal Au-Ag. Although these three systems can have some genetic and spatial overlap, their features and origins are sufficiently distinct to allow for separate evaluation. These three types of mineralizing systems account for most of the important lode metallic mineral deposits discovered in northern Nevada since the middle of the Nineteenth Century. They are important sources of gold, silver, copper, lead, zinc, and molybdenum. Pluton-related polymetallic systems also have potential for producing platinum-group elements (PGE).

For addition information and details, see:

Wallace, A.R., Ludington, S., Mihalasky, M.J., Peters, S.G., Theodore, T.G., Ponce, D.A., John, D.A., and Berger, B.R., 2004, Assessment of metallic mineral resources in the Humboldt River Basin, Northern Nevada, with a section on PGE potential of the Humboldt mafic complex by M.L. Zientek, G.B. Sidder, and R.A. Zierenberg: U.S. Geological Survey Bulletin B-2218, CD-ROM.


How was the data set created?

  1. From what previous works were the data drawn?

Stewart and Carlson (1978) (source 1 of 5)

Stewart, J.H., and Carlson, J.E., 1978, Geologic map of Nevada: none none, U.S. Geological Survey and Nevada Bureau of Mines and Geology, Denver, CO.

Type_of_Source_Media: paper

Source_Scale_Denominator: 500000

Source_Contribution:

This dataset was used to create and proof the digital version of the geologic map of Nevada (Raines and others, 1996), which in turn was used to create the following evidence maps for WofE and WLR modeling:

1) Lithodiversity. Lithodiversity for the geologic map of Nevada was generated by counting the number of unique map units in a square moving window that is 2.5-by-2.5 km in dimension. Lithodiversity was calculated by centering the window on each cell, counting the number of unique geologic map units within the neighborhood, assigning the number to the center cell, and then incrementing the window by one cell. The lithodiversity map was reclassified such that each map class value (an integer) represents diversity. For example, lithodiversity map class 5 represents five geologic units within a sample neighborhood. Mihalasky (2001) and Mihalasky and Bonham-Carter (1999; 2001) discuss methods of preparation and processing of lithodiversity. For analysis and modeling, this dataset is considered to have a resolution of 1,000 meters.

2) Pluton proximity. The plutonic rocks, represented in terms of unit abbreviations from the geologic map of Nevada, include Tri, Tmi, Ti, Tr2, Tr1, TJgr, Tgr, Mzgr, Kgr, KJd, Jgr, TRgr, and TRlgr. These units range in age from Middle-Late Triassic to late Miocene, but with respect to total area covered, the units predominantly are Mesozoic. Plutonic and intrusive bodies were buffered with a distance interval of 1 km. The plutons were included as part of the first buffer. For analysis and modeling, this dataset is considered to have a resolution of 1,000 meters.

For the specific evidence maps used to create this mineral-resource assessment tract map, see the "Entity_and_Attribute_Information / Overview_Description" section of the metadata below, and more detailed information in "Entity_and_Attribute_Information / Detailed_Description".

For references cited and additional information on the evidence maps listed above, see Wallace and others (2004, Chapter 2).

Wallace, A.R., Ludington, S., Mihalasky, M.J., Peters, S.G., Theodore, T.G., Ponce, D.A., John, D.A., and Berger, B.R., 2004, Assessment of metallic mineral resources in the Humboldt River Basin, Northern Nevada, with a section on PGE potential of the Humboldt mafic complex by M.L. Zientek, G.B. Sidder, and R.A. Zierenberg: U.S. Geological Survey Bulletin B-2218, CD-ROM.

Raines and others (1996) (source 2 of 5)

Raines, G.L., Sawatzky, D.L., and Connors, K., 1996, Great Basin geoscience data base: U.S. Geological Survey Digital Data Series DDS-41, U.S. Geological Survey, Denver, CO.

Type_of_Source_Media: CD-ROM

Source_Scale_Denominator: 500000

Source_Contribution:

This dataset was used to create the following evidence maps for WofE and WLR modeling:

1) Lithodiversity. Lithodiversity for the geologic map of Nevada was generated by counting the number of unique map units in a square moving window that is 2.5-by-2.5 km in dimension. Lithodiversity was calculated by centering the window on each cell, counting the number of unique geologic map units within the neighborhood, assigning the number to the center cell, and then incrementing the window by one cell. The lithodiversity map was reclassified such that each map class value (an integer) represents diversity. For example, lithodiversity map class 5 represents five geologic units within a sample neighborhood. Mihalasky (2001) and Mihalasky and Bonham-Carter (1999; 2001) discuss methods of preparation and processing of lithodiversity. For analysis and modeling, this dataset is considered to have a resolution of 1,000 meters.

2) Pluton proximity. The plutonic rocks, represented in terms of unit abbreviations from the geologic map of Nevada, include Tri, Tmi, Ti, Tr2, Tr1, TJgr, Tgr, Mzgr, Kgr, KJd, Jgr, TRgr, and TRlgr. These units range in age from Middle-Late Triassic to late Miocene, but with respect to total area covered, the units predominantly are Mesozoic. Plutonic and intrusive bodies were buffered with a distance interval of 1 km. The plutons were included as part of the first buffer. For analysis and modeling, this dataset is considered to have a resolution of 1,000 meters.

For the specific evidence maps used to create this mineral-resource assessment tract map, see the "Entity_and_Attribute_Information / Overview_Description" section of the metadata below, and more detailed information in "Entity_and_Attribute_Information / Detailed_Description".

For references cited and additional information on the evidence maps listed above, see Wallace and others (2004, Chapter 2).

Wallace, A.R., Ludington, S., Mihalasky, M.J., Peters, S.G., Theodore, T.G., Ponce, D.A., John, D.A., and Berger, B.R., 2004, Assessment of metallic mineral resources in the Humboldt River Basin, Northern Nevada, with a section on PGE potential of the Humboldt mafic complex by M.L. Zientek, G.B. Sidder, and R.A. Zierenberg: U.S. Geological Survey Bulletin B-2218, CD-ROM.

Folger (2000) (source 3 of 5)

Folger, H.W., 2000, Analytical results and sample locations of reanalyzed NURE stream-sediment and soil samples for the Humboldt River Basin mineral-environmental assessment, northern Nevada: Open-File Report 00-421, U.S. Geological Survey, Denver, CO.

Type_of_Source_Media: CD-ROM

Source_Contribution:

This dataset was used to create the following evidence maps for WofE and WLR modeling:

1) As-frequency. Represents As concentration (partial digestion) that was processed in the frequency domain. The concentration values were log transformed (base-10) and converted to a continuous raster surface with 1,000-meter cell size using a minimum-curvature spatial interpolator. The data were resolved into several textural components by computing the spatial frequency structure of the surface, then deriving a series of band-pass frequency filters to decompose the surface in the frequency domain into distinct layers, each with varying degrees of smoothness. The residual As anomaly evidence map corresponds to subtraction of the long- and medium-wavelength components of the signal. The rationale for this method, and the preparation and processing of this dataset, are discussed in greater detail in Wallace and others (2004, Chapter 5). For analysis and modeling, this dataset is considered to have a resolution of 1,000 meters.

2) Cu-Pb-Zn signature. Calculated from Cu, Pb, and Zn concentrations (total digestion). The concentration values were (1) log transformed (base 10), (2) normalized to a unitless, standardized scale (a "z-score"; see McGrew and Monroe, 1993, and Theodore and others, 2000), and (3) converted to three continuous raster surfaces with 1,000-meter cell size using an inverse distance spatial interpolator. For surface interpolation, a fixed sampling radius of 15 km and a distance-decay rate that diminishes with the square of the distance was used. The three datasets were then re-scaled between zero and one, where the z-score value of zero was set to 0.5. After the re-scaling, any values greater than one or less than zero were set to 1 and 0, respectively. Processed in this way, z-score value of zero, which represents the mean log value of a given element concentration, is assigned a fuzzy membership score of 0.5. The datasets were then mathematically combined using a fuzzy logic "OR" operator (Bonham-Carter, 1994), which selects the maximum value at a given cell when the rasters are combined, yielding an elevated Cu-Pb-Zn signature value wherever a high value in any one of the three elements is present. The output of the fuzzy operator is the evidence map. For analysis and modeling, this dataset is considered to have a resolution of 1,000 meters.

For the specific evidence maps used to create this mineral-resource assessment tract map, see the "Entity_and_Attribute_Information / Overview_Description" section of the metadata below, and more detailed information in "Entity_and_Attribute_Information / Detailed_Description".

For references cited and additional information on the evidence maps listed above, see Wallace and others (2004, Chapter 2).

Wallace, A.R., Ludington, S., Mihalasky, M.J., Peters, S.G., Theodore, T.G., Ponce, D.A., John, D.A., and Berger, B.R., 2004, Assessment of metallic mineral resources in the Humboldt River Basin, Northern Nevada, with a section on PGE potential of the Humboldt mafic complex by M.L. Zientek, G.B. Sidder, and R.A. Zierenberg: U.S. Geological Survey Bulletin B-2218, CD-ROM.

Ponce (1997) (source 4 of 5)

Ponce, D.A., 1997, Gravity data of Nevada: U.S. Geological Survey Digital Data Series DDS-42, U.S. Geological Survey, Denver, CO.

Type_of_Source_Media: CD-ROM

Source_Contribution:

This dataset was used to create the following evidence maps for WofE and WLR modeling:

1) Basement gravity terranes. Reflect regions of similar anomaly features or geophysical fabric. The terranes were derived from the inspection of isostatic and basement gravity maps, and maximum horizontal gradients of basement gravity anomalies. The terranes were outlined in vector format and converted to raster with a 500-meter cell size. The preparation and processing of these datasets is discussed in greater detail in Wallace and others (2004, Chapter 6). For analysis and modeling, this dataset is considered to have a resolution of 1,000 meters.

2) Basement gravity lineaments. Reflect abrupt lateral variations in the density of basement rocks. The lineaments were derived from basement gravity anomalies and their maximum horizontal gradients. The linear features were buffered at a distance interval of 2 km. The features were included as part of the first buffer. The preparation and processing of these datasets is discussed in greater detail in Wallace and others (2004, Chapter 6). For analysis and modeling, this dataset is considered to have a resolution of 2,000 meters.

3) Depth to basement. Reflects thickness of Cenozoic cover deposits. Isostatic residual gravity data were used to produce a map of the thickness of Cenozoic deposits based on assumed variations of density with depth in these deposits (Chapter 6; Jachens and others, 1996). The depth to basement map does not serve as an evidence map proper. Rather, it is used as an overlay on the mineral-resource tract maps to mask out areas that are covered by Cenozoic deposits that are more than 1 km thick. The preparation and processing of these datasets is discussed in Wallace and others (2004, Chapter 6). For analysis and modeling, this dataset is considered to have a resolution of 2,000 meters.

For the specific evidence maps used to create this mineral-resource assessment tract map, see the "Entity_and_Attribute_Information / Overview_Description" section of the metadata below, and more detailed information in "Entity_and_Attribute_Information / Detailed_Description".

For references cited and additional information on the evidence maps listed above, see Wallace and others (2004, Chapter 2).

Wallace, A.R., Ludington, S., Mihalasky, M.J., Peters, S.G., Theodore, T.G., Ponce, D.A., John, D.A., and Berger, B.R., 2004, Assessment of metallic mineral resources in the Humboldt River Basin, Northern Nevada, with a section on PGE potential of the Humboldt mafic complex by M.L. Zientek, G.B. Sidder, and R.A. Zierenberg: U.S. Geological Survey Bulletin B-2218, CD-ROM.

McFaul and others (2000) (source 5 of 5)

McFaul, E.J., Mason, G.T., Jr., Ferguson, W.B, 2000, U.S. Geological Survey mineral databases; MRDS and MAS/MILS: U.S. Geological Survey Digital Data Series DDS0052, U.S. Geological Survey, Denver, CO.

Type_of_Source_Media: CD-ROM

Source_Contribution:

This dataset was used to create the following evidence maps for WofE and WLR modeling:

1) Skarn proximity. As an extension of the classification of mineral deposits done in the Winnemucca-Surprise assessment (Peters and others, 1996), the HRB mineral-resource assessment team classified 550 MRDS mineral sites as skarn related, based on Cox and Singer (1986) deposit model types 14a, 18a, 18b, 18c, 18d, 18e, and 18f. The mineral sites were buffered at distance interval of 1 km. The sites were included as part of the first buffer. For analysis and modeling, this dataset is considered to have a resolution of 1,000 meters.

For the specific evidence maps used to create this mineral-resource assessment tract map, see the "Entity_and_Attribute_Information / Overview_Description" section of the metadata below, and more detailed information in "Entity_and_Attribute_Information / Detailed_Description".

For references cited and additional information on the evidence maps listed above, see Wallace and others (2004, Chapter 2).

Wallace, A.R., Ludington, S., Mihalasky, M.J., Peters, S.G., Theodore, T.G., Ponce, D.A., John, D.A., and Berger, B.R., 2004, Assessment of metallic mineral resources in the Humboldt River Basin, Northern Nevada, with a section on PGE potential of the Humboldt mafic complex by M.L. Zientek, G.B. Sidder, and R.A. Zierenberg: U.S. Geological Survey Bulletin B-2218, CD-ROM.

  1. How were the data generated, processed, and modified?

Date: 2000 (process 1 of 1)

The mineral-resource tracts were created by merging knowledge-driven-derived tracts (non-permissive and permissive) and data-driven-derived tracts (favorable and prospective). The non-permissive and permissive tracts were delineated by Singer (1996). The favorable and prospective tracts were delineated using WofE and WLR data-driven analysis and modeling techniques, which can be subdivided into three main procedures: (1) measurement of spatial association between the training sites and the evidence maps, (2) optimization of the evidence maps for prediction (creation of predictor patterns), and (3) combination of the predictor patterns to create favorability maps.

Construction of the final mineral-resource assessment tract maps consisted of four steps: (1) clipping of the favorable and prospective tracts to the extent of geochemistry evidence map coverage (see Wallace and others, 2004, Chapters 2 and 5), (2) clipping of the favorable and prospective tracts to knowledge-driven-delineated permissive tract (Singer, 1996), (3) merging of the clipped favorable and prospective tracts with the knowledge-driven-delineated permissive tract, and (4) masking out of areas where the depth to basement is greater than 1 km (see Wallace and others, 2004, Chapter 6).

For a more thorough discussion of WofE and WLR analysis and modeling techniques, see Bonham-Carter (1989, 1994), Agterberg and others (1990), Wright and Bonham-Carter (1996), Raines (1999), Mihalasky (2001), Raines and Mihalasky (2002), Wallace and others (2004)

Agterberg, F.P., Bonham-Carter, G.F., Wright, D.F., 1990, Statistical pattern integration for mineral exploration, in Gaál, G., and Merriam, D.F., eds., Computer Applications in Resource Estimation Prediction and Assessment of Metals and Petroleum: New York, Pergamon Press, p. 1-12.

Bonham-Carter, G.F., 1994, Geographic Information Systems for Geoscientists: Modelling with GIS (Computer Methods in the Geosciences Volume 13): Tarrytown, New York, Pergamon Press/Elsevier Science Publications, 398 p.

Bonham-Carter, G.F., Agterberg, F.P., and Wright, D.F., 1989, Weights of evidence modelling: A new approach to mapping mineral potential, in Agterberg, F.P., and Bonham-Carter, G.F., eds., Statistical Applications in the Earth Science: Geological Survey of Canada, Paper 89-9, p. 171-183.

Mihalasky, M.J., 2001, Mineral potential modelling of gold and silver mineralization in the Nevada Great Basin-A GIS-based analysis using weights of evidence: U.S. Geological Survey Open-File Report 01-291, 448 p., [<http://geopubs.wr.usgs.gov/open-file/of01-291/]>.

Raines, G.L., 1999, Evaluation of weights of evidence to predict epithermal-gold deposits in the Great Basin of the western United States: Natural Resources Research, v. 8, p. 257-276.

Raines, G.L., and Mihalasky, M.J., 2002, A reconnaissance method for regional-scale mineral-resource assessment, based exclusively on geologic-map data: Natural Resources Research, v. 11, no. 4, p. 241-248.

Singer, D.A., 1996, An analysis of Nevada's metal-bearing mineral resources: Nevada Bureau of Mines and Geology Open-File Report 96-2, <http://www.nbmg.unr.edu/dox/ofr962/cover.pdf>

Wallace, A.R., Ludington, S., Mihalasky, M.J., Peters, S.G., Theodore, T.G., Ponce, D.A., John, D.A., and Berger, B.R., 2004, Assessment of metallic mineral resources in the Humboldt River Basin, Northern Nevada, with a section on PGE potential of the Humboldt mafic complex by M.L. Zientek, G.B. Sidder, and R.A. Zierenberg: U.S. Geological Survey Bulletin B-2218, CD-ROM.

Wright, D.F., and Bonham-Carter, G.F., 1996, VHMS favourability mapping with GIS-based integration models, Chisel Lake-Anderson Lake area, in Bonham-Carter, G.F., Galley, A. G., and Hall, G. E. M., eds., EXTECH I: A Multidisciplinary Approach to Massive Sulphide Research in the Rusty Lake-Snow Lake Greenstone Belts, Manitoba: Geological Survey of Canada Bulletin 426, p. 339-376, 387-401.

Person who carried out this activity:

Alan Wallace
U. S. Geological Survey
Research Geologist
C/O Mackay School of Mines
Reno, NV 89557
USA

1-775-784-5789 (voice)
1-775-784-5079 (FAX)
alan@usgs.gov

Hours_of_Service: No set hours

Contact_Instructions:

Originator is no longer with U.S. Geological Survey Western Mineral Resources Team. Contact Alan Wallace (alan@usgs.gov) or Mark Mihalasky at mihalasky@hotmail.com.

  1. What similar or related data should the user be aware of?

Mihalasky, Mark J. , and Moyer, Lorre A. , 2004, Spatial databases of the Humboldt Basin mineral resource assessment, northern Nevada: Open-File Report 2004-1245, U. S. Geological Survey, Denver, CO.

Online Links:

Other_Citation_Details:

Mineral assessment tract grid and metadata created at U. S. Geological Survey, Reno Field Office, Reno, Nevada.

This is part of the following larger work.

Wallace, A.R., Ludington, S., Mihalasky, M.J., 2004, Assessment of metallic mineral resources in the Humboldt River Basin, northern Nevada: U.S. Geological Survey Bulletin 2004-2218, U. S. Geological Survey, Denver, CO.

Online Links:


How reliable are the data; what problems remain in the data set?

  1. How well have the observations been checked?

Considering the measures described below, the tracts delineated meet USGS standards. A Student T test of the confidence in this map indicates a moderate degree of confidence, approximately greater than 90%, that the reported logistic-regression posterior probabilities are not zero in the permissive, favorable, and prospective tracts. A quantitative assessment of error and uncertainty related to WofE and WLR analysis and modeling is provided in the form of four dBASE tables that are generated by Arc-SDM:

1) woe - Spatial associations among the evidence and training sites.

2) woevar - Standard deviations for the spatial weights of association.

3) prb - Results of pair-wise and overall tests for conditional independence.

4) lrcoef - Logistic regression coefficients

The contents of these tables is embedded in this metadata document under the "Quantitative_Attibute_Accuracy_Explanation" section below. The following information will help with interpretation of these tables:

Conditional Independence:

An important assumption made in WofE modeling is that the evidence layers be conditionally independent (CI) of one another with respect to the training sites. Evidence layer dependencies were tested for using a pairwise and an overall goodness-of-fit test, both of which make use of the observed versus the predicted number of observations (training sites). The pairwise test measures CI between all possible pairings of evidence maps (with respect to the training sites) by calculating the chi-square statistic for each map pair. The overall test is a measure of the CI between all of the evidence maps in a model as a whole. The overall test consists a comparison between the predicted number of training sites to the observed number (observed/predicted, referred to as the "CI ratio"). For this mineral-resource assessment tract map, the overall CI ratio was low (0.48), indicating strong conditional dependencies that are reflected by highly inflated WofE posterior probablity values. Therefore, WLR was used to calculate the posterior probability and to define the assessment tract classes.

Error and Uncertainty:

An important aspect to interpreting a favorability map is recognizing and quantifying the uncertainty inherent to the posterior probabilities. The two primary sources of uncertainty are: (1) the uncertainty due to variances in weight estimates (W+ and W-); and (2) the uncertainty due to one or more of the evidence maps having incomplete coverage (i.e., missing data).

In addition to the uncertainties due to weights and missing data variances, a relative confidence of the posterior probability can be calculated by dividing the posterior probability by its standard deviation, which is an informal Student t-test to determine whether the posterior probability is greater than zero for a selected level of statistical significance. This confidence test is often more useful than the weights or missing data variances because it indicates the degree of confidence to which the posterior probabilities are meaningful, as opposed to being an artifact of chance effects or interactions. Care should be taken when interpreting the relative certainty because it is based on a normal distribution and sensitive to CI violations.

For additional information on conditional independence, error, and uncertainty, see:

Agterberg, F.P., Bonham-Carter, G.F., Cheng, Q., and Wright, D.F., 1993, Weights of evidence modeling and weighted logistic regression for mineral potential mapping, in Davis, J.C., and Herzfeld, U.C., eds., Computers in Geology, 25 Years of Progress: Oxford, England, Oxford University Press, p. 13-32.

Bonham-Carter, G.F., 1994, Geographic Information Systems for Geoscientists: Modelling with GIS (Computer Methods in the Geosciences Volume 13): Tarrytown, New York, Pergamon Press/Elsevier Science Publications, 398 p.

Wallace, A.R., Ludington, S., Mihalasky, M.J., Peters, S.G., Theodore, T.G., Ponce, D.A., John, D.A., and Berger, B.R., 2004, Assessment of metallic mineral resources in the Humboldt River Basin, Northern Nevada, with a section on PGE potential of the Humboldt mafic complex by M.L. Zientek, G.B. Sidder, and R.A. Zierenberg: U.S. Geological Survey Bulletin B-2218, CD-ROM.

  1. How accurate are the geographic locations?

This mineral-resource assessment tract map is a derivative product that was generated by integrating a number of geological, geochemical, and geophysical evidence layers (the specific layers are summarized in the "Entity_and_Attribute_Information / Overview_Description" section of the metadata below, and detailed in "Entity_and_Attribute_Information / Detailed_Description"). Because of compounding spatial error associated with the integration operation, the mineral-resource assessment tract map is considered to have an accuracy of approximately 1:1,000,000. In practical terms, the ground resolution of the map is about 2 km. For additional details, see the "Identification_Information / Use_Constraints" and "Data_Quality_Information / Logistical_Consistency_Report" sections of the metadata above.

  1. How accurate are the heights or depths?

not applicable

  1. Where are the gaps in the data? What is missing?

The Humboldt River Basin (HRB) assessment was carried out for the whole of northern Nevada, north of latitude 38 degrees 30 minutes, but focused primarily on the HRB and adjoining areas. The permissive tract, previously delineated in an earlier assessment by Singer (1996), was used for the HRB assessment but truncated at 38 degrees 30 minutes north latitude. Favorable and prospective mineral-resource assessment tracts, products of the HRB assessment, were merged with the Singer (1996) permissive tracts but are truncated to the extent of (1) the geochemistry evidence map coverage and (2) the extent of the permissive tract (for details, see Wallace and others, 2004, Chapter 2).

Singer, D.A., ed., 1996, An analysis of Nevada's metal-bearing mineral resources: Nevada Bureau of Mines and Geology Open-file Report 96-2, <http://www.nbmg.unr.edu/dox/ofr962/cover.pdf>.

Wallace, A.R., Ludington, S., Mihalasky, M.J., Peters, S.G., Theodore, T.G., Ponce, D.A., John, D.A., and Berger, B.R., 2004, Assessment of metallic mineral resources in the Humboldt River Basin, Northern Nevada, with a section on PGE potential of the Humboldt mafic complex by M.L. Zientek, G.B. Sidder, and R.A. Zierenberg: U.S. Geological Survey Bulletin B-2218, CD-ROM.

  1. How consistent are the relationships among the observations, including topology?

This mineral-resource assessment tract map is a derivative product created by a unique conditions overlap (see Bonham-Carter, 1994) among several evidence layers, which are summarized in the "Entity_and_Attribute_Information / Overview_Description" section of the metadata below, and detailed in "Entity_and_Attribute_Information / Detailed_Description". As such, its spatial fidelity is related to the compounded fidelity of all evidence layers that were combined.

For an overview of the spatial fidelity of the tract map, see the "Identification_Information / Use_Contraints" section of the metadata above. For information on the spatial fidelity of each evidence layer, see source data information as outlined and referenced in Wallace and others (2004). For the fidelity related to analysis and modeling, the see the results of tests for conditional independence, error, and uncertainty provided under the "Attibute_Accuracy" section of the metadata above.

Bonham-Carter, G.F., 1994, Geographic Information Systems for Geoscientists: Modelling with GIS (Computer Methods in the Geosciences Volume 13): Tarrytown, New York, Pergamon Press/Elsevier Science Publications, 398 p.

Wallace, A.R., Ludington, S., Mihalasky, M.J., Peters, S.G., Theodore, T.G., Ponce, D.A., John, D.A., and Berger, B.R., 2004, Assessment of metallic mineral resources in the Humboldt River Basin, Northern Nevada, with a section on PGE potential of the Humboldt mafic complex by M.L. Zientek, G.B. Sidder, and R.A. Zierenberg: U.S. Geological Survey Bulletin B-2218, CD-ROM.


How can someone get a copy of the data set?

Are there legal restrictions on access or use of the data?

Access_Constraints: none

Use_Constraints:

It is strongly recommended that the user consult the "Identification_Information / Description / Supplemental_Information" section of the metadata, as well as Chapter 2 of Wallace and others (2004), for information about the expert analysis, modeling techniques, and the vocabulary used in this mineral-resource assessment, as this information is essential for properly interpreting and applying the assessment tract maps.

Scale of Usage:

Both the expert and data-driven components of the HRB mineral-resource assessment were conducted using data that range in scale from 1:250,000 to about 1:1,000,000. These scales were chosen because many of the data sets used in the assessment were at those scales, most notably the geologic map of Nevada and various derivative maps. Manipulation of these data as part of the expert analysis and data-driven modeling processes, as well as the combination of these different scale data, has further decreased their collective resolution and accuracy to nearer 1:1,000,000. As such, the mineral-resource assessment tract maps should not be used at a scale larger than 1:1,000,000. In practical terms, the ground resolution of the assessment maps is about 2 km. Therefore, any boundary between two assessment tracts has no greater resolution than 2 km. In keeping with the regional concept of this assessment, and the possible use of the maps for land-use planning purposes, any small areas of interest that lie along or near a tract boundary should be evaluated with care and with supplementary data that are consistent with the large scale and resolution of the area being examined. Similar considerations should be applied when working with small, isolated assessment tract areas, as they are less reliably classified, as discussed further below.

The purpose of the assessment was to delineate broad areas in northern Nevada and the HRB that are relatively more or less likely to contain undiscovered mineral deposits. This purpose, and the regional scale of the data used to achieve it, should be kept in mind when using this assessment and the digital mineral-resource assessment maps. Use of maps at larger scales to examine small areas in detail diverges from the concept and purpose of the assessment and the assessment maps.

What the Prospective and Favorable Tracts Represent:

The data used for data-driven modeling were selected because they reflect geological, geochemical, and geophysical attributes common to known pluton-related polymetallic, sedimentary rock-hosted Au-Ag, and epithermal Au-Ag mineral deposits and mineralizing processes in the assessment area. The resulting prospective and favorable tracts are areas in which the data have an optimal combination of attributes found at known deposits. This does not mean that mineralizing processes took place in those areas, but rather that the models identify areas that may warrant further, more detailed evaluation before land-use or mining-related decisions are made. Conversely, it is considered unlikely that a mineralizing process took place outside of that area.

The assessment tract maps show the more optimal data combinations, from the standpoint of the possible presence of a mineralized system, as "prospective" or "favorable." These areas range in size from small to large. The smallest areas (less than or equal to about 2 square km) are artifacts of the data-driven modeling process; these should be considered "noise" and thus warrant little or no further scrutiny. Some prospective and favorable areas are extensive and represent relatively large areas that have many attributes common to known deposits. Many of these areas have known mineral deposits of the type being assessed. The confidence that these areas have been correctly classified as favorable or prospective is 90% or greater (see Chapter 2, sections "Data-Driven Component" and "Modifications During Data-Driven Modeling" in Wallace and others, 2004).

The assessment tract maps do not define the specific locations of potential deposits within the broad prospective and favorable areas. Given the scale and nature of the data used for the assessment, it is possible that the data do not reflect isolated mineralizing systems and mineral deposits that are outside of prospective or favorable areas. The surface expression of the largest known mineralizing systems in northern Nevada, such as the cluster of large pluton-related polymetallic systems at Battle Mountain, is several tens of square kilometers. The surface expressions of other known, in some cases large, mineral deposits in the region have a somewhat to substantially smaller footprint. In addition, the vertical dimension of some deposits, such as the Meikle deposit in the Carlin trend or the Ken Snyder deposit in Midas, is equal to or greater than their horizontal dimension at the surface. Therefore, more detailed studies of small areas within prospective and favorable tracts require data and concepts relevant to that scale of assessment, similar to the methods employed by the mining industry to evaluate specific properties.

  1. Who distributes the data set? (Distributor 1 of 1)

U.S. Geological Survey
USGS Information Services (Open-File Report Sales)
Denver, CO 80225
USA

303-202-4700 (voice)
303-202-4188 (FAX)
custserv@usgs.gov

Hours_of_Service: 8:00 AM to 4:00 PM (Mountain Time)

Contact_Instructions:

This report is available in an electronic format at the following URL = <http://pubs.usgs.gov/of/2004/1245>

  1. What's the catalog number I need to order this data set?

Downloadable online data and CD-ROM as <http://pubs.usgs.gov/of/2004/1245>

  1. What legal disclaimers am I supposed to read?

Although all data and software have been used by the U.S. Geological Survey (USGS), no warranty, expressed or implied, is made by the USGS as to the accuracy of the data and related materials and (or) the functioning of the software. The act of distribution shall not constitute any such warranty, and no responsibility is assumed by the USGS in the use of these data, software, or related materials. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

  1. How can I download or order the data?
    • Availability in digital form:

Data format:

Compressed ESRI grid ESRI GRid

Network links:

<http://pubs.usgs.gov/of/2004/1245>

Media you can order:

CD-ROM (format ISO 9660 Level 2 standard)

Note: All platform compatibility, Intel Pentium or equivalent processor.

    • Cost to order the data: Subject to change
  1. Is there some other way to get the data?

CD-ROM of Open-File Report 2004-1245 can be purchased through the USGS Information Services (Open-File Report Sales).

  1. What hardware or software do I need in order to use the data set?

The CD-ROM was produced in accordance with the ISO 9660 Level 2 standard. The data can be accessed using any software capable of reading an ESRI grid (raster) or image file.


Who wrote the metadata?

Dates:

Last modified: 03-Mar-2004

Metadata author:

Author: Mark J. Mihalasky; Contact: Alan R. Wallace
U. S. Geological Survey
Research Geologist
C/O Mackay School of Mines
Reno, NV 89557
USA

1-775-784-5789 (voice)
1-775-784-5079 (FAX)
alan@usgs.gov

Contact_Instructions:

The originator of the dataset, Mark Mihalasky, is no longer with U. S. Geological Survey. Contact Alan Wallace (alan@usgs.gov) or Mark Mihalasky at mihalasky@hotmail.com.

Metadata standard:

FGDC Content Standards for Digital Geospatial Metadata (FGDC-STD-001-1998)

Metadata extensions used:

·         <http://www.esri.com/metadata/esriprof80.html>


Generated by mp version 2.7.33 on Tue May 18 10:59:50 2004
updated October 2, 2006 (mfd)