Assessment method for epithermal gold deposits in northeast Washington State using weights-of-evidence GIS modeling

Boleneus, D.E.; Raines, Gary L.; Causey, J. Douglas; Bookstrom, Arthur A.; Frost, Thomas P.; Hyndman, Paul C.

doi:10.3133/ofr01501

Cited by 14 publications

(13 citation statements)

References 13 publications

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“…Initially developed for non-spatial application in medical diagnosis [23], this method was extended to spatially predict mineral resources [10]. The method has also been used for the predictions of volcanogenic massive sulphides [2], gold occurrences [3,8,9], delineating favourable areas for epithermal gold deposits [6], tracts for mineral resource assessment [20] and porphyry copper potential mapping [24]. There are also known successful WofE applications for the analysis of groundwater vulnerability [4] and the investigation of spatial associations between natural seismicity and faults [11], analysis of the winter habitat relationships of red deer [5], modelling Native American sacred sites [12] and mapping cliff instabilities [25].…”

Section: Description Of the Methodsmentioning

confidence: 99%

Probabilistic Modelling of Cattle Farm Distribution in Australia

Emelyanova

Donald

Miron

et al. 2008

Environ Model Assess

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A probabilistic Bayesian method called weights of evidence (WofE) was used to develop a synthetic dataset of cattle farm locations at a national scale across Australia. The synthetic dataset was required for the modelling of livestock movements with a view to assessing biosecurity implications. The WofE method is based on the analysis of spatial relationships between evidential patterns with respect to an event, such as the actual location of a farm. The evidential patterns of cattle farms were derived from maps of land use, land tenure, drainage systems, roads, settlements and long-term averaged rainfall. These evidential patterns were used for delineating and ranking land areas suitable for cattle farming. For each evidential pattern statistics such as a positive weight, a negative weight and a contrast were calculated for estimating the degree of correlation between the evidential patterns and known farm locations. The integrated evidential patterns of known farms were then used for estimating posterior probabilities and splitting land into five different classes according to its suitability for farming.

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Section: Description Of the Methodsmentioning

confidence: 99%

Probabilistic Modelling of Cattle Farm Distribution in Australia

Emelyanova

Donald

Miron

et al. 2008

Environ Model Assess

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“…The other is the cumulative method of calculating weights for data or information represented as relational values (ordinal data, intervals, ratios, etc.) in a map (Boleneus et al 2001).…”

Section: Analysis By Weights-of-evidence (Wofe)mentioning

confidence: 98%

Targeting of Gold Deposits in Amazonian Exploration Frontiers using Knowledge- and Data-Driven Spatial Modeling of Geophysical, Geochemical, and Geological Data

Magalhães¹,

Filho²

2011

Surv Geophys

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This paper reports the application of weights-of-evidence, artificial neural networks, and fuzzy logic spatial modeling techniques to generate prospectivity maps for gold mineralization in the neighborhood of the Amapari Au mine, Brazil. The study area comprises one of the last Brazilian mineral exploration frontiers. The Amapari mine is located in the Maroni-Itaicaiúnas Province, which regionally hosts important gold, iron, manganese, chromite, diamond, bauxite, kaolinite, and cassiterite deposits. The Amapari Au mine is characterized as of the orogenic gold deposit type. The highest gold grades are associated with highly deformed rocks and are concentrated in sulfide-rich veins mainly composed of pyrrhotite. The data used for the generation of gold prospectivity models include aerogeophysical and geological maps as well as the gold content of stream sediment samples. The prospectivity maps provided by these three methods showed that the Amapari mine stands out as an area of high potential for gold mineralization. The prospectivity maps also highlight new targets for gold exploration. These new targets were validated by means of detailed maps of gold geochemical anomalies in soil and by fieldwork. The identified target areas exhibit good spatial coincidence with the main soil geochemical anomalies and prospects, thus demonstrating that the delineation of exploration targets by analysis and integration of indirect datasets in a geographic information system (GIS) is consistent with direct prospecting. Considering that work of this nature has never been developed in the Amazonian region, this is an important example of the applicability and functionality of geophysical data and prospectivity analysis in regions where geologic and metallogenetic information is scarce.

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“…Weights-of-evidence models have been extensively applied to map potential of a variety of mineral deposits, for example, (1) vein-type gold deposits in Nova Scotia, Canada (Bonham-Carter et al, 1988); (2) Carlin-type epithermal gold deposits in Nevada, U.S.A. (Mihalasky, 1999); (3) epithermal gold deposits in the Great Basin of the western United States (Raines, 1999), in northeast Washington State, U.S.A. (Boleneus et al, 2001) and in the Baguio district, the Philippines (Carranza and Hale, 2000); (4) porphyry copper deposits in British Columbia, Canada (Singh et al, 1993) and in the Benguet province, the Philippines (Carranza and Hale, 2002); (5) VHMS-type base metal sulfide deposits in greenstone terrains of Manitoba, Canada (Wright and Bonham-Carter, 1996); and (6) SEDEX-type base metal deposits in the Aravalli province, India (Porwal and Hale, 2000).…”

Section: Introductionmentioning

confidence: 99%

“…Porwal and Hale (2000) applied the weights-of-evidence analysis of multi-class evidential maps to generate a favorability map for SEDEX-type base metal deposits in Aravalli province, India. Boleneus et al (2001) used multi-class categorical and cumulative data in their weights-of-evidence model for assessment of epithermal gold deposit potential in northeast Washington State, U.S.A.…”

Section: Introductionmentioning

confidence: 99%

“…The part of the curve above the higher inflection point represents a much lower (almost nil) increase in cumulative posterior probability compared to the increase in cumulative area, and low posterior probabilities in a very high percentage of the study area. The two inflection points on each curve were used as threshold values to reclassify the posterior probability maps into favorability maps showing favorable, permissive, and non-permissive zones (Boleneus et al, 2001). Favorable zones are those with posterior probability corresponding to the part of curve below the lower inflection point.…”

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confidence: 99%

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Extended Weights-of-Evidence Modelling for Predictive Mapping of Base Metal Deposit Potential in Aravalli Province, Western India

Porwal¹,

Carranza

Hale

2001

Exploration and Mining Geology

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Approaches to mineral potential mapping based on weights of evidence generally use binary maps, whereas, real-world geospatial data are mostly multi-class in nature. The consequent reclassification of multi-class maps into binary maps is a simplification that might result in a loss of information. This paper describes results of using multi-class evidential maps in an extended weights-of-evidence model vis-à-vis results of using binary evidential maps in a simple-weights-ofevidence model. The study area in the south-central part of Aravalli province (western India) hosts a number of SEDEX-type base metal deposits in Proterozoic supracrustal rocks. Recognition criteria for base metal deposits were represented as both multi-class and binary evidential maps. The known mineral deposits were divided into two subsets, viz., the training and the validation subsets. The training subset was used to calculate, for the evidential maps, the weights, contrasts, and posterior probabilities and their variances. The distributions of expected frequencies of base metal deposits estimated from the posterior probabilities and the observed frequencies were compared using standard goodness-of-fit tests to verify conditional independence of the input evidential maps. The posterior probabilities from both the models were mapped and interpreted to classify the study area into zones favorable, permissive, and non-permissive for base metal deposit occurrence. As compared to the simple weights-of-evidence model, the extended weights-of-evidence model results in more robust and finely differentiated posterior probabilities in favorable and permissive zones and has a better prediction rate. The results also reveal that the statistical properties of the weights of evidence, the contrasts, and the posterior probabilities are not significantly degenerated by using multi-class evidential maps in weights-of-evidence modelling.

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Assessment method for epithermal gold deposits in northeast Washington State using weights-of-evidence GIS modeling

Cited by 14 publications

References 13 publications

Probabilistic Modelling of Cattle Farm Distribution in Australia

Probabilistic Modelling of Cattle Farm Distribution in Australia

Targeting of Gold Deposits in Amazonian Exploration Frontiers using Knowledge- and Data-Driven Spatial Modeling of Geophysical, Geochemical, and Geological Data

Extended Weights-of-Evidence Modelling for Predictive Mapping of Base Metal Deposit Potential in Aravalli Province, Western India

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