Citation: | Sheng-yuan ZHANG, Qiu-ming CHENG, Zhi-jun CHEN. Omnibus Weights of Evidence Method Implemented in GeoDAS GIS for Information Extraction and Integration. Journal of Earth Science, 2008, 19(4): 404-409. |
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 WofE 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 WofE 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 validated using a case study of mineral potential prediction in Gejiu (个旧) mineral district, Yunnan (云南), China.
Agterberg, F. P., Bonham-Carter, G. F., Wright, D. F., 1990. Statistical Pattern Integration for Mineral Exploration. In: Ga′al, G., Merriam, D. F., eds., Computer Applications in Resource Estimation Prediction and Assessment of Metals and Petroleum. Computers and Geology, 7: 1-21. |
Ali, K., Cheng, Q., Chen, Z., 2007. Multifractal Power Spectrum and Singularity Analysis for Modeling Stream Sediment Geochemical Distribution Patterns to Identify Anomalies Related to Gold Mineralization in Yunnan Province, South China. Geochemistry: Exploration, Environment, Analysis, 7: 293-301. doi: 10.1144/1467-7873/06-116 |
Bonham-Carter, G. F., 1994. Geographic Information Systems for Geoscientists: Modeling with GIS. Computer Methods in the Geosciences, 13: 398. |
Bonham-Carter, G. F., Agterberg, F. P., Wright, D. F., 1988. Integration of Geological Datasets for Gold Exploration in Nova Scotia. Photogrammetric Engineering and Remote Sensing, 54 (11): 1585-1592. |
Cheng, Q., 2007. Mapping Singularities with Stream Sediment Geochemical Data for Prediction of Undiscovered Mineral Deposits in Gejiu, Yunnan Province, China. Ore Geology Reviews, 32: 314-324. doi: 10.1016/j.oregeorev.2006.10.002 |
Cheng, Q., Agterberg, F. P., 1999. Fuzzy Weights of Evidence Method and Its Application in Mineral Potential Mapping. Natural Resources Research, 8 (1): 27-35. doi: 10.1023/A:1021677510649 |
Cheng, Q., Chen, Z., Ali, K., 2007. Application of Fuzzy Weights of Evidence Method in Mineral Resources Assessment for Gold in Zhenyuan District, Yunnan Province, China. Earth Science—Journal of China University of Geosciences, 32 (2): 175-184 (in Chinese with English Abstract). |
Cheng, Q., Zhang, S. Y., 2002. Fuzzy Weights of Evidence Method Implemented in GeoDAS GIS for Information Extraction and Integration for Prediction of Point Events. Int. Geosciences and Remote Sensing Symposium, 5: 2933-2935. doi: 10.1109/IGARSS.2002.1026826 |
Coolbaugh, M. F., Raines, G. L., Zehner, R. E., 2007. Assessment of Exploration Bias in Data-Driven Predictive Models and the Estimation of Undiscovered Resources. Natural Resources Research, 16 (2): 199-207. doi: 10.1007/s11053-007-9037-6 |
Kemp, L. D., Bonham-Carter, G. F., Raines, G. L., et al., 2001. Arc-SDM: ArcView Extension for Spatial Data Modeling Using Weights of Evidence, Logistic Regression, Fuzzy Logic and Neural Network Analysis. http://ntserv.gis.nrcan.gc.ca/sdm/. |
Yu, C., Tang, Y., Shi, P., et al., 1988. The Dynamic System of 665 Endogenic Ore Formation in Gejiu Tin-Polymetallic Ore Region, Yunnan Province. China University of Geosciences Press, Wuhan. 394 (in Chinese with English Abstract). |
Zhang, S. Y., Wu, Q., Cheng, Q., et al., 2006. Weights of Evidence Method Based on Fuzzy Training Layer and Its Application in Desertification Assessment. Earth Science—Journal of China University of Geosciences, 31 (3): 389-393 (in Chinese with English Abstract). |