近年来,开展覆盖区矿产资源预测成为全球开始关注的热点,如何应用新技术、新理论、新方法,实现覆盖区找矿预测水平和找矿效果的提高,有效地降低找矿工作的勘查风险,是今后找矿工作的主要研究方向。通过引入非线性理论,加强多学科联合攻...近年来,开展覆盖区矿产资源预测成为全球开始关注的热点,如何应用新技术、新理论、新方法,实现覆盖区找矿预测水平和找矿效果的提高,有效地降低找矿工作的勘查风险,是今后找矿工作的主要研究方向。通过引入非线性理论,加强多学科联合攻关,创新覆盖区矿产资源预测理论、方法和关键信息技术,在国际通用的GeoD AS GIS平台上,利用广义自相似理论。展开更多
Weights of evidence （WofE） is an artificial intelligent method for integration of information from diverse sources for predictive purpose in supporting decision making. This method has been commonly used to predict ...Weights of evidence （WofE） is an artificial intelligent method for integration of information from diverse sources for predictive purpose in supporting decision making. This method has been commonly used to predict point events by integrating point training layer and binary or ternary evidential layers （multiclass evidence less commonly used）. Omnibus weights of evidence integrates fuzzy training layer and diverse evidential layers. This method provides new features in comparison with the ordinary Wore method. This new method has been implemented in a geographic information system-geophysical data analysis system and the method includes the following contents： （1） dual fuzzy weights of evidence （DFWofE）, in which training layer and evidential layers can be treated as fuzzy sets. DFWofE can be used to predict not only point events but also area or line events. In this model a fuzzy training layer can be defined based on point, line, and areas using fuzzy membership function; and （2） degree-of-exploration model for WorE is implemented through building a degree of exploration map. This method can be used to assess possible spatial correlations between the degree of exploration and potential evidential layers. Importantly, it would also make it possible to estimate undiscovered resources, if the degree of exploration map is combined with other models that predict where such resources are most likely to occur. These methods and relevant systems were vafidated using a case study of mineral potential prediction in Gejiu （个旧） mineral district, Yunnan （ 云南）, China.展开更多
文摘近年来,开展覆盖区矿产资源预测成为全球开始关注的热点,如何应用新技术、新理论、新方法,实现覆盖区找矿预测水平和找矿效果的提高,有效地降低找矿工作的勘查风险,是今后找矿工作的主要研究方向。通过引入非线性理论,加强多学科联合攻关,创新覆盖区矿产资源预测理论、方法和关键信息技术,在国际通用的GeoD AS GIS平台上,利用广义自相似理论。
基金supported by the National Natural Science Foundation of China (No. 40638041)National Key Technology R&D Program (No. 2006BAB01A01)+2 种基金Project of China Geological Survey (No. 1212010633910)the National High Technology Research and Development Program of China (Nos. 2006AA06Z115, 2006AA06Z113)State Key Laboratory of Geological Processes and Mineral Resources (No. GPMR2007-12)
文摘Weights of evidence （WofE） is an artificial intelligent method for integration of information from diverse sources for predictive purpose in supporting decision making. This method has been commonly used to predict point events by integrating point training layer and binary or ternary evidential layers （multiclass evidence less commonly used）. Omnibus weights of evidence integrates fuzzy training layer and diverse evidential layers. This method provides new features in comparison with the ordinary Wore method. This new method has been implemented in a geographic information system-geophysical data analysis system and the method includes the following contents： （1） dual fuzzy weights of evidence （DFWofE）, in which training layer and evidential layers can be treated as fuzzy sets. DFWofE can be used to predict not only point events but also area or line events. In this model a fuzzy training layer can be defined based on point, line, and areas using fuzzy membership function; and （2） degree-of-exploration model for WorE is implemented through building a degree of exploration map. This method can be used to assess possible spatial correlations between the degree of exploration and potential evidential layers. Importantly, it would also make it possible to estimate undiscovered resources, if the degree of exploration map is combined with other models that predict where such resources are most likely to occur. These methods and relevant systems were vafidated using a case study of mineral potential prediction in Gejiu （个旧） mineral district, Yunnan （ 云南）, China.
