• The Korean Journal of Quaternary Research
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• v.17 no.2
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• pp.153-156
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• 2003

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.662-663
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• 2004

In general, mineral resources prospect is performed in several methods including geological survey, geological structure analysis, geochemical exploration, airborne geophysical exploration and remote sensing, but data collected through these methods are usually not integrated for analysis but used separately. Therefore we compared various data integration techniques and generated final mineral resources potentiality map.

• Proceedings of the Korean Vacuum Society Conference
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• 2004.08a
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• pp.190-190
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• 2004

• Proceedings of the KSEEG Conference
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• 2005.04a
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• pp.184-188
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• 2005

• Proceedings of the Korean Nuclear Society Conference
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• 2005.10a
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• pp.737-738
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• 2005

• The Korean Journal of Quaternary Research
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• v.21 no.2
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• pp.1-3
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• 2007

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.66-67
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• 2008

Most mineral resources are located in subsurface but mineral exploration starts with a step of investigation in wide-area to find evidence of buried ores. Conventional technique for exploration on wide-area as a preliminary survey is an observation using naked eyes by geologist or chemical analysis using lots of samples obtained from target area. Hyperspectral remote sensing can overcome those subjective and time consuming survey and can produce mineral resources distribution map. Precise resource map requires information of mineral distribution in a subpixellevel because mineral is distributed as rock components or narrow veins. But most hyperspectral data is composed of pixels of several meters or more than ten meters scale. We reviewed subpixel unmixing algorithms which have been used for geological field and tested detection ability with Hyperion imagery, geological map and seven spectral curves of mineral and rock specimens which were obtained from study areas.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2003.09a
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• pp.132-135
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• 2003

Solution chemistry, sorbate chemistry, and sorbent chemistry were widely investigated to find important factors that affect PAH sorption on mineral surfaces and to elucidate its microscopic mechanism. The solution chemistry, pH and ionic strength caused measurable change of HOC sorption reaction to minerals. The detectable change of Ka occurred at a pH region crossing the PZC (Point of Zero Charge) of each mineral. The PAH hydrophobicity, one of sorbate chemistry, was observed to have a strong correlation with PAM sorption to mineral. Mineral surface area was not found to be a predominant factor controlling PAH sorption. The mineral type might be more likely to play a crucial role in controlling the PAH sorption behavior. The CEC (Cation Exchange Capacity) of mineral, representing surface charge density, has meaningful correlation with regression slope of sorption coefficients (log $K_{d}$) versus aqueous activity coefficients (log Υ$_{w}$).).).

• Economic and Environmental Geology
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• v.42 no.1
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• pp.85-94
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• 2009

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.3
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• pp.1-13
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• 2010

The aim of this study is to analyze gold-silver mineral potential in the Taebaeksan mineralized district, Korea using a Geographic Information System(GIS) and an artificial neural network(ANN) model. A spatial database considering Au and Ag deposit, geology, fault structure and geochemical data of As, Cu, Mo, Ni, Pb and Zn was constructed for the study area using the GIS. The 46 Au and Ag mineral deposits were randomly divided into a training set to analyze mineral potential using ANN and a test set to verify mineral potential map. In the ANN model, training sets for areas with mineral deposits and without them were selected randomly from the lower 10% areas of the mineral potential index derived from existing mineral deposits using likelihood ratio. To support the reliability of the Au-Ag mineral potential map, some of rock samples were selected in the upper 5% areas of the mineral potential index without known deposits and analyzed for Au, Ag, As, Cu, Pb and Zn. As the result, No. 4 of sample exhibited more enrichments of all elements than the others.