Fuzzy logic fusion of W-Mo exploration data from Seobyeog-ri, Korea
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There are continuing changes in the mineral exploration methodology, specially in the processing, analysis, and interpretation of geological exploration data. Many new data integration techniques based on GISs allow multiple sets of exploration survey data to be processed and fused quickly and precisely, so that the assessment on the final target(s) deposit can be more accurate than conventional approaches.
Raw exploration data gathered in field, or old archived exploration data in local government mining offices, are usually in differing data formats, which usually require editing and preprocessing of the data prior to an information representation step utilizing a chosen mathematical tool. In this study, we collected several sets of old geological exploration data, digitized, processed and geocoded each layer of spatial data, and then digitally represented using fuzzy logic for later fusion. In the Seobyeog-ri (Korea) study area, there was also and old tungsten mine, which we used as a ground-truthing control point. The target proposition adopted is that “there is a tungsten (W) and molibdenum (Mo) mineralization” in the study area and the mineral deposit model used was a simple skarn type contact mineralization.
Although several authors have recently used fuzzy logie approaches for fusion of mineral exploration data, selecting an optimum fuzzy operator has always been a difficult task. It has become apparent that fuzzy operators depend very much on the deposit model and types of spatial data to be integrated. In this study, three types of operators, fuzzy OR, fuzzy AND, and fuzzy γ-operators are tested and selected ones are used in combination, some in series and others in parallel. The unbiased processing results obtained in this study indicate that the most probable target area is clearly outlined in the vicinity of the old Sangdong tungsten (W) mine, and have confirmed our initial proposition. However, the initial assumption of a skarn type mineralization could not be further verified requiring further investigation.
Key wordsfuzzy set theory data fusion mineral exploration GIS skarn fuzzy logic
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