Weights-of-evidence and logistic regression modeling of magmatic nickel sulfide prospectivity in the Yilgarn Craton, Western Australia
Weights-of-evidence and logistic regression modeling of magmatic nickel sulfide prospectivity in the Yilgarn Craton, Western Australia
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DOI：
10.1016/j.oregeorev.2010.04.002
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发表时间：
2010-11
影响因子：
3.3
通讯作者：
A. Porwal;I. González-Álvarez;V. Markwitz;T. McCuaig;A. Mamuse
中科院分区：
文献类型：
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作者：
A. Porwal;I. González-Álvarez;V. Markwitz;T. McCuaig;A. Mamuse
在GIS环境下，采用贝叶斯证据权和逻辑回归模型对西澳大利亚Yilgarn克拉通的岩浆硫化镍矿绿岩带进行了区域尺度的远景建模。模型的输入变量由衍生GIS层组成，这些层被用作Yilgarn岩浆硫化镍矿床可测绘勘探标准的代理。在已知的165个克拉通矿床中，约有70%用于训练模型；剩下的30%用于验证模型，因此，必须像未被发现一样对待。证据权重和逻辑回归模型分别对占克拉通绿岩带总面积约9%的远景带的71.4%和81.6%的有效矿床进行了分类。逻辑回归模型的优越性能归因于它能够适应输入预测器映射之间的条件依赖关系，并提供较少的前瞻性偏差估计。
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-of-evidence 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.