Abstract
Extracting and synthesizing information from existing and massive amounts of geology spatial data sets is of great scientific significance and has considerable value in its applications. To make mineral exploration less expensive, more efficient, and more accurate, it is important to move beyond traditional concepts and establish a rapid, efficient, and intelligent method of predicting the existence and location of minerals. This paper describes a case-based reasoning (CBR) method for mineral prospectivity mapping that takes spatial features of geology data into account and offers an intelligent approach. This method include a metallogenic case representation that combines spatial and attribute features, metallogenic case-based storage organization, and a metallogenic case similarity retrieval model. The experiments were performed in the eastern Kunlun Mountains, China using CBR and weights-of-evidence (WOE), respectively. The results show that the prediction accuracy of the CBR is higher than that of the WOE.
Highlights
Mineral prospectivity analysis and quantitative resource estimation have been recognized as important when integrating multi-source geology spatial data in recent years [1]
This paper describes a case-based reasoning (CBR) method for mineral prospectivity mapping that takes spatial features of geology data into account and offers an intelligent approach
The experiment was performed in the eastern Kunlun Mountains, China to predict the existence of potential iron deposits using case-based reasoning and weights-of-evidence, respectively
Summary
Mineral prospectivity analysis and quantitative resource estimation have been recognized as important when integrating multi-source geology spatial data in recent years [1]. The fuzzy logic [11, 12], artificial neural networks [13,14] and the Fractal method [15] have been applied in the assessment of mineral resources potential These methods promote the efficiency and effectiveness of mineral resource prospecting, their algorithms are unable to accumulate knowledge, and lack intelligent reasoning. The metallogenic geological conditions and spatial distribution of discovered and typical deposits can be used to construct a historical case-base for mineral prospectivity analysis. Some researchers have begun applying case-based reasoning (CBR) to the environment, urban planning, and land use. The experiment was performed in the eastern Kunlun Mountains, China to predict the existence of potential iron deposits using case-based reasoning and weights-of-evidence, respectively
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