重回帰分析の適用による秋田県北鹿地域における黒鉱鉱床有望地の抽出
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概要
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In order to extract promising areas of mineral resources, many studies have used stochastic or statistical methods such as weight of evidence, fuzzy logic and neural network. With the development of computers, the GIS (geographic information system), which is effective for spatial analysis of different kinds of data in an area, has been available to process enormous quantities of exploration data through the use of a personal computer.The Hokuroku district covering a 31 km × 44 km area in northern Akita, Japan, was chosen as the site for a case study. This area is famous for having many kuroko deposits generated in Miocene, a kind of volcanic massive sulfide deposits. For the study area, nine kinds of exploration indexes regarding geological features, deposits and geochemical data were examined in combination with the deposit formation and geophysical data covering the study area, such as gravity, airborne magnetic, and lineament density data. Initially, 12 kinds of exploration indexes were digitized in order to handle with the GIS and to construct a spatial database. The area was gridded at 1km spacing, and multiple regression analysis and variable increase method were used to estimate the basement structure and the deposit positions as well as to extract promising exploration areas in the horizontal plane.The regression function proved to be analytically useful, and seven effective kinds of exploration indexes were specified. In particular, two kinds of regression coefficients were large. These included vein type deposits, in which the host rock is in the shape of hanging wall formations of mineralized stratum (K10) and the alteration index (K12). Based on the multiple regression analysis, 80 % of all the ore bodies may be discovered in the 27.3 % portions of the study area, which is considered to be conspicuously efficient as compared with the index overlay model.
- 社団法人 資源・素材学会の論文
- 2003-05-25
著者
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