Weights-of-evidence and logistic regression modeling of magmatic nickel sulfide prospectivity in the Yilgarn Craton, Western Australia
Bayesian weight-of-evidence and logistic regression models are implemented in a GIS environment for regional-scale prospectivity modeling of greenstone belts in the Yilgarn Craton, Western Australia, for magmatic nickel sulfide deposits. The input variables for the models consisted of derivative G...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2016
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/11408/893 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1779905655496245248 |
|---|---|
| author | Porwal, A. González-Álvarez, I. Markwitz, V. McCuaig, T.C. Mamuse, Antony |
| author_facet | Porwal, A. González-Álvarez, I. Markwitz, V. McCuaig, T.C. Mamuse, Antony |
| author_sort | Porwal, A. |
| collection | DSpace |
| description | Bayesian weight-of-evidence and logistic regression models are implemented in a GIS environment for
regional-scale prospectivity modeling of greenstone belts in the Yilgarn Craton, Western Australia, for
magmatic nickel sulfide deposits. The input variables for the models consisted of derivative GIS layers that
were used as proxies for mappable exploration criteria for magmatic nickel sulfide deposits in the Yilgarn.
About 70% of the 165 known deposits of the craton were used to train the models; the remaining 30% was used to validate the models and, therefore, had to be treated as if they had not been discovered. The weights-ofevidence and logistic regression models, respectively, classify 71.4% and 81.6% validation deposits in prospective zones that occupy about 9% of the total area occupied by the greenstone belts in the craton. The superior performance of the logistic regression model is attributed to its capability to accommodate conditional dependencies amongst the input predictor maps, and provide less biased estimates of prospectivity. |
| format | Article |
| id | ir-11408-893 |
| institution | My University |
| language | English |
| publishDate | 2016 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | ir-11408-8932022-06-27T13:49:06Z Weights-of-evidence and logistic regression modeling of magmatic nickel sulfide prospectivity in the Yilgarn Craton, Western Australia Porwal, A. González-Álvarez, I. Markwitz, V. McCuaig, T.C. Mamuse, Antony Yilgarn Craton GIS-based prospectivity mapping Weights-of-evidence Logistic regression, magmatic nickel sulfide deposits Bayesian weight-of-evidence and logistic regression models are implemented in a GIS environment for regional-scale prospectivity modeling of greenstone belts in the Yilgarn Craton, Western Australia, for magmatic nickel sulfide deposits. The input variables for the models consisted of derivative GIS layers that were used as proxies for mappable exploration criteria for magmatic nickel sulfide deposits in the Yilgarn. About 70% of the 165 known deposits of the craton were used to train the models; the remaining 30% was used to validate the models and, therefore, had to be treated as if they had not been discovered. The weights-ofevidence and logistic regression models, respectively, classify 71.4% and 81.6% validation deposits in prospective zones that occupy about 9% of the total area occupied by the greenstone belts in the craton. The superior performance of the logistic regression model is attributed to its capability to accommodate conditional dependencies amongst the input predictor maps, and provide less biased estimates of prospectivity. 2016-04-19T14:39:55Z 2016-04-19T14:39:55Z 2010 Article 0169-1368 http://hdl.handle.net/11408/893 en Ore Geology Reviews;Vol. 38; p. 184–196 none Elsevier |
| spellingShingle | Yilgarn Craton GIS-based prospectivity mapping Weights-of-evidence Logistic regression, magmatic nickel sulfide deposits Porwal, A. González-Álvarez, I. Markwitz, V. McCuaig, T.C. Mamuse, Antony Weights-of-evidence and logistic regression modeling of magmatic nickel sulfide prospectivity in the Yilgarn Craton, Western Australia |
| title | Weights-of-evidence and logistic regression modeling of magmatic nickel sulfide prospectivity in the Yilgarn Craton, Western Australia |
| title_full | Weights-of-evidence and logistic regression modeling of magmatic nickel sulfide prospectivity in the Yilgarn Craton, Western Australia |
| title_fullStr | Weights-of-evidence and logistic regression modeling of magmatic nickel sulfide prospectivity in the Yilgarn Craton, Western Australia |
| title_full_unstemmed | Weights-of-evidence and logistic regression modeling of magmatic nickel sulfide prospectivity in the Yilgarn Craton, Western Australia |
| title_short | Weights-of-evidence and logistic regression modeling of magmatic nickel sulfide prospectivity in the Yilgarn Craton, Western Australia |
| title_sort | weights-of-evidence and logistic regression modeling of magmatic nickel sulfide prospectivity in the yilgarn craton, western australia |
| topic | Yilgarn Craton GIS-based prospectivity mapping Weights-of-evidence Logistic regression, magmatic nickel sulfide deposits |
| url | http://hdl.handle.net/11408/893 |
| work_keys_str_mv | AT porwala weightsofevidenceandlogisticregressionmodelingofmagmaticnickelsulfideprospectivityintheyilgarncratonwesternaustralia AT gonzalezalvarezi weightsofevidenceandlogisticregressionmodelingofmagmaticnickelsulfideprospectivityintheyilgarncratonwesternaustralia AT markwitzv weightsofevidenceandlogisticregressionmodelingofmagmaticnickelsulfideprospectivityintheyilgarncratonwesternaustralia AT mccuaigtc weightsofevidenceandlogisticregressionmodelingofmagmaticnickelsulfideprospectivityintheyilgarncratonwesternaustralia AT mamuseantony weightsofevidenceandlogisticregressionmodelingofmagmaticnickelsulfideprospectivityintheyilgarncratonwesternaustralia |