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International Journal of Mining and Geo-Engineering، جلد ۵۷، شماره ۱، صفحات ۱۱-۲۵
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عنوان انگلیسی HEBF strategy: A hybrid evidential belief function in geospatial data analysis for mineral potential mapping
چکیده انگلیسی مقاله In integrating geospatial datasets for mineral potential mapping (MPM), the uncertainty model of MPM can be inferred from the Dempster – Shafer rules of combination. In addition to generating the uncertainty model, evidential belief functions (EBFs) present the belief, plausibility, and disbelief of MPM, whereby four models can be simultaneously utilized to facilitate the interpretation of mineral favourability output. To investigate the functionality and applicability of the EBFs, we selected the Naysian porphyry copper district located on the Urmia – Dokhtar magmatic belt in the northeast of Isfahan city, central Iran. Multidisciplinary datasets- that are geochemical and geophysical data, ASTER satellite images, Quickbird, and ground survey- were designed in a geospatial database to run MPM. Implementing the Dempster law through the intersection (And) and :union: (OR) operators led to different MPM performances. To amplify the accuracy of the generated favourability maps, a combinatory EBFs technique was applied in three ways: (1) just OR operator, (2) just And operator, and (3) combination of And and OR operators. The plausibility map (as mineral favourability map) was compared to Cu productivity values derived from drilled boreholes, where the MPM accuracy of the hybrid method was higher than each operator. Of note, the success rate of the hybrid method validated by 21 boreholes was about 84%, and it demarcates high favourability zones occupying 0.67 km2 of the studied area.
کلیدواژه‌های انگلیسی مقاله hybrid method,Evidential Believe Functions (EBFs),Geospatial Dataset,Porphyry copper,Naysian District
نویسندگان مقاله Mahyadin Mohammadpour |
School of Mining Engineering, College of Engineering, University of Tehran, Iran

Abbas Bahroudi |
School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran

Maysam Abedi |
Department of Mining Engineering, University of Tehran

نشانی اینترنتی https://ijmge.ut.ac.ir/article_86879_949fbcda0916e71a759ab817d7c2fc00.pdf
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