A Comparison of Modified Fuzzy Weights of Evidence, Fuzzy Weights of Evidence, and Logistic Regression for Mapping Mineral Prospectivity
A Comparison of Modified Fuzzy Weights of Evidence, Fuzzy Weights of Evidence, and Logistic Regression for Mapping Mineral Prospectivity
复制标题
修正证据模糊权重、证据模糊权重和逻辑回归绘制矿物前景图的比较
DOI：
10.1007/s11004-013-9496-8
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发表时间：
2014-10
影响因子：
2.6
通讯作者：
Zuo, Renguang
中科院分区：
文献类型：
--
作者：
Zhang, Daojun;Agterberg, Frits;Cheng, Qiuming;Zuo, Renguang
证据权重法和逻辑回归法是两种最常用的矿产勘查方法。逻辑回归模型总是产生无偏估计，无论证据变量是否相对于目标变量是条件独立的，而证据权重模型具有易于解释和实现的建模过程。它已被证明，存在一个模型结合证据权重和逻辑回归，具有这两个优点。在这项研究中，三个模型组成的修正模糊证据权重，模糊证据权重，逻辑回归进行了比较，相互映射的矿产远景。改进的证据模型模糊权值保留了证据模型模糊权值和Logistic回归模型的优点，其优点是：（1）改进的证据模型模糊权值估计的矿床预测数量与Logistic回归模型的预测数量接近，（2）改进的证据模型模糊权值能够处理缺失数据。该方法是福建省铁矿远景填图的有效工具。
Weights of evidence and logistic regression are two of the most popular methods for mapping mineral prospectivity. The logistic regression model always produces unbiased estimates, whether or not the evidence variables are conditionally independent with respect to the target variable, while the weights of evidence model features an easy to explain and implement modeling process. It has been shown that there exists a model combining weights of evidence and logistic regression that has both of these advantages. In this study, three models consisting of modified fuzzy weights of evidence, fuzzy weights of evidence, and logistic regression are compared with each other for mapping mineral prospectivity. The modified fuzzy weights of the evidence model retains the advantages of both the fuzzy weights of the evidence model and the logistic regression model; the advantages being (1) the predicted number of deposits estimated by the modified fuzzy weights of evidence model is nearly equal to that of the logistic regression model, and (2) it can deal with missing data. This method is shown to be an effective tool for mapping iron prospectivity in Fujian Province, China.
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DOI：
10.4095/128125
发表时间：
1990
期刊：
--
影响因子：
--
作者：
F. Agterberg;G. Bonham-Carter
通讯作者：
F. Agterberg;G. Bonham-Carter
影响因子：
5.4
作者：
E. Carranza
通讯作者：
E. Carranza
影响因子：
7
作者：
Seong‐Hoon Cho;N. Poudyal;R. Roberts
通讯作者：
Seong‐Hoon Cho;N. Poudyal;R. Roberts
影响因子：
3.9
作者：
N. Regmi;J. Giardino;J. Vitek
通讯作者：
N. Regmi;J. Giardino;J. Vitek
影响因子：
3.3
作者：
A. Porwal;I. González-Álvarez;V. Markwitz;T. McCuaig;A. Mamuse
通讯作者：
A. Porwal;I. González-Álvarez;V. Markwitz;T. McCuaig;A. Mamuse