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 Sedimentary Rock-Hosted Au-Ag 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 Sedimentary Rock-Hosted Au-Ag 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 "sed" 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 Sedimentary Rock-Hosted Au-Ag 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:

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:

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

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

What does it look like?
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.

What is the general form of this data set?

Geospatial_Data_Presentation_Form: ESRI integer raster digital data

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.

How does the data set describe geographic features?

sed

Sedimentary Rock-Hosted Au-Ag Metallic Mineral-Resource Assessment Tract - 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:	330
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:	1
Maximum:	8872813
Units:	none
Resolution:	1

ASSPAT

Presence or absence of predictor pattern asspat - Arsenic anomaly, As-spatial, representing NURE As concentration (partial digestion) that was processed in the spatial 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
-99	predictor pattern missing
0	predictor pattern absent
1	predictor pattern present

BANA

Presence or absence of predictor pattern bana - Ba/Na anomaly, calculated from NURE Ba and Na 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
-99	predictor pattern missing
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

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

NE_LINEARS

Presence or absence of predictor pattern ne_linears - NE (northeast) linear features, representing two corridor regions that envelop the Crescent Valley-Independence (CVIL) and Getchell (GLF) lineaments. (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

NVGEOL51

Presence or absence of predictor pattern nvgeol51 - Geologic units from the geologic map of Nevada. Includes the following units: Osv, PMh, Cc, Oc, PPa, Pcd, St, Os,MDs, Ct, Dc, Ch, SOc, TRc, Dsl, Tbr, and OCc. (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
-99	predictor pattern missing
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

THSWIN

Presence or absence of predictor pattern thswin - Proximity to thrust faults between upper- and lower-plate tectonic units and structural windows of the Roberts Mountains thrust fault. (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

TRRN

Presence or absence of predictor pattern trrn - Lithotectonic-terrane units, representing geologic map units that, in combination, represent specific allochthonous terranes in Nevada. Terrane units include Roberts Mountains, Vinini, and North America (includes structural windows). (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
-99	predictor pattern missing
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:	10233060000.00
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:	65
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:	0.66141993
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:	0.12747242
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:	-20.35827169
Maximum:	0.66962831
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:	-13.86760000
Maximum:	7.16030000
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:	0.07298778
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:	0.31207227
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:	0.31593738
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:	0.09873000
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:	10.78356000
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:	0.01378000
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 sedimentary rock-hosted Au-Ag 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 = 0.0015; favorable-prospective posterior probability boundary = 0.006; for details, see Wallace and others, 2004, Chapters 2 and 8). 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 sedimentary rock-hosted Au-Ag 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 = 0.0015; favorable-prospective posterior probability boundary = 0.006; for details, see Wallace and others, 2004, Chapters 2 and 8). 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 sedimentary rock-hosted Au-Ag favorable and prospective mineral-resource assessment tracts:

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

bana - Ba/Na anomaly, calculated from NURE Ba and Na concentrations (total digestion).

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

lithodvr - Lithodiversity of the geologic map of Nevada.

ne_linears - NE (northeast) linear features, representing two corridor regions that envelop the Crescent Valley-Independence (CVIL) and Getchell (GLF) lineaments.

nvgeol51 - Geologic units from the geologic map of Nevada. Includes the following units: Osv, PMh, Cc, Oc, PPa, Pcd, St, Os,MDs, Ct, Dc, Ch, SOc, TRc, Dsl, Tbr, and OCc.

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).

thswin - Proximity to thrust faults between upper- and lower-plate tectonic units and structural windows of the Roberts Mountains thrust fault.

trrn - Lithotectonic-terrane units, representing geologic map units that, in combination, represent specific allochthonous terranes in Nevada. Terrane units include Roberts Mountains, Vinini, and North America (includes structural windows).

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:

Who produced the data set?

Who are the originators of the data set? (may include formal authors, digital compilers, and editors)

Mark J. Mihalasky
Lorre A. Moyer

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.

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?

From what previous works were the data drawn?

Stewart and Carlson (1978) (source 1 of 6)

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) Geologic units. The geologic map of Nevada consists of 101 map units. As described in Stewart and Carlson (1978), the map units are combinations of one or more formations or geologic units of similar age and geologic context that were identified during larger-scale geologic mapping. In some cases, such as map units that consist entirely of Tertiary basalts, the map units are good proxies for lithologic units. In many cases, however, the inclusion of several disparate lithologies into one map unit, such as shale, sandstone, and limestone in some Paleozoic-age map units, limits a direct comparison to lithology (see discussion in Wallace and others, 2004, Chapter 8). For analysis and modeling, this dataset is considered to have a resolution of 1,000 meters.

2) 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.

3) 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).

Raines and others (1996) (source 2 of 6)

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

Lahren and others (in press) (source 3 of 6)

Lahren, M.M., Schweickert, R.A., and Connors,, unpublished (written communication, 1999), Preliminary map of allochthonous tectonic units in Nevada and eastern California: none none, none, none.

Other_Citation_Details:

A preliminary map of allochthonous tectonic units was obtained in vector format from K.A. Connors (written communication, 1999).

Type_of_Source_Media: electronic transfer

Source_Scale_Denominator: 750000

Source_Contribution:

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

1) Lithotectonic-terrane units. The lithotectonic-terrane units map shows geologic map units that, in combination, represent specific allochthonous terranes in Nevada. These terranes include, but are not limited to, the Roberts Mountains, Golconda, and Fencemaker allochthons. The definitions of the allochthonous terranes were based on many studies reported in the published literature. The locations of the units and the terranes that they define are based on the geologic units shown in the geologic map of Nevada (Stewart and Carlson, 1978; Raines and others, 1996). For analysis and modeling, this dataset is considered to have a resolution of 1,000 meters.

2) Thrust proximity. Defined as the proximity to thrust faults between upper- and lower-plate tectonic units and structural windows of the Roberts Mountains thrust fault. As described in Wallace and others (2004, Chapter 8), this thrust was instrumental in the localization of many sedimentary rock-hosted Au-Ag deposits. Thrust faults related to other allochthons were not considered in this assessment. The thrust faults were extracted and buffered with a distance interval of 1 km. The faults 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 references cited and additional information on the evidence maps listed above, see Wallace and others (2004, Chapter 2).

U.S. Geological Survey (1996) (source 4 of 6)

U.S. Geological Survey, EROS Data Center Distributed Active , 1996, North American 30 arc-second DEM: none none, U.S. Geological Survey, EROS Data Center in Sioux Falls, SD.

Other_Citation_Details:

Data downloaded in 1996 from <http://edcftp.cr.usgs.gov>, <ftp://edcftp.cr.usgs.gov/pub/data/gtopo30/global/>

Type_of_Source_Media:

on-line, <http://edcftp.cr.usgs.gov>, <ftp://edcftp.cr.usgs.gov/pub/data/gtopo30/global/>

Source_Scale_Denominator: 1600000

Source_Contribution:

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

1) NE (northeast) linear features. Consists of the Crescent Valley-Independence (CVIL) and Getchell (GLF) lineaments. Two corridor regions that envelop the CVIL (Peters, 1998; Theodore and Peters, 1998) and the Getchell mineral trend were interpreted by the assessment team from LANDSAT MSS imagery (60-meter resolution) and shaded relief of topography (30 arc-second, approximately 801 meter resolution). The corridor regions were outlined in vector format and converted to raster with 2,000-meter cell size. For analysis and modeling, this dataset is considered to have a resolution of 2,000 meters.

For refere