On reviewing the characteristics of deep mineral exploration, this article elaborates on the necessity of employing quantitative prediction to reduce uncertainty. This is caused by complexity of mineral deposit formational environments and mineralization systems as increase of exploration depth and incompleteness of geo-information from limited direct observation. The authors wish to share the idea of ＂seeking difference＂ principle in addition to the ＂similar analogy＂ principle in deep mineral exploration, especially the focus is on the new ores in depth either in an area with discovered shallow mineral deposits or in new areas where there are no sufficient mineral deposit models to be compared. An on-going research project, involving Sn and Cu mineral deposit quantitative prediction in the Gejiu （个旧） area of Yunnan （云南） Province, China, was briefly introduced to demonstrate how the ＂three-component＂ （geoanomaly-mineralization diversity-mineral deposit spectrum） theory and non-linear methods series in conjunction with advanced GIS technology, can be applied in multi-scale and multi-task deep mineral prospecting and quantitative mineral resource assessment.
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.
Two phenomena of similar objects with different spectra and different objects with similar spectrum often result in the difficulty of separation and identification of all types of geographical objects only using spectral information. Therefore, there is a need to incorporate spatial structural and spatial association properties of the surfaces of objects into image processing to improve the accuracy of classification of remotely sensed imagery. In the current article, a new method is proposed on the basis of the principle of multiple-point statistics for combining spectral information and spatial information for image classification. The method was validated by applying to a case study on road extraction based on Landsat TM taken over the Chinese Yellow River delta on August 8, 1999. The classification results have shown that this new method provides overall better results than the traditional methods such as maximum likelihood classifier （MLC）.
