• 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.

• Spatial Information Research
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• v.14 no.1 s.36
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• pp.129-150
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• 2006

Mineral potential mapping is an important procedure in mineral resource assessment. The purpose of this study is to analyze mineral potential using weight of evidence model and a Geographic Information System (GIS) environment to identify areas that have not been subjected to the same degree of exploration. For this, a variety of spatial geological data were compiled, evaluated and integrated to produce a map of potential mineral in the Gangreung area, Korea. for this, a spatial database considering mineral deposit, topographic, geologic, geophysical and geochemical data was constructed for the study area using a GIS. The used mineral deposits were non-metallic(Kaolin, Porcelainstone, Silicastone, Mica, Nephrite, Limestone and Pyrophyllite) deposits of sedimentary type. The factors relating to mineral deposits were the geological data such as lithology and fault structure, geochemical data, including the abundance of Al, As, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Si, Sr, V, Zn, $Cl^-,\;F^-,\;{PO_4}^{3-},\;{NO_2}^-,\;{NO_3}^-,\;SO_{42-}$, Eh, PH and conductivity and geophysical data, including the Bouguer and magnetic anomalies. These factors were used with weight of evidence model to analyze mineral potential. Probability models using the weight of evidence were applied to extract the relationship between mineral deposits and related factors, and the ratio were calculated. Then the potential indices were calculated by summation of the likelihood ratio and mineral potential maps were constructed from Geographic Information System (GIS). The mineral potential maps were then verified by comparison with the known mineral deposit areas. The result showed the 85.66% in prediction accuracy.

• Proceedings of the KSRS Conference
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• v.2
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• pp.710-712
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• 2006

The aim of this study was to analyze limestone deposits potential using an artificial neural network and a Geographic Information System (GIS) environment to identify areas that have not been subjected to the same degree of exploration. For this, a variety of spatial geological data were compiled, evaluated and integrated to produce a map of potential deposits in the Gangreung area, Korea. A spatial database considering deposit, topographic, geologic, geophysical and geochemical data was constructed for the study area using a GIS. The factors relating to 44 limestone deposits were the geological data, geochemical data and geophysical data. These factors were used with an artificial neural network to analyze mineral potential. Each factor’s weight was determined by the back-propagation training method. Training area was applied to analyze and verify the effect of training. Then the mineral deposit potential indices were calculated using the trained back-propagation weights, and potential map was constructed from GIS data. The mineral potential map was then verified by comparison with the known mineral deposit areas. The verification result gave accuracy of 87.31% for training area.

• Geophysics and Geophysical Exploration
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• v.17 no.4
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• pp.209-215
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• 2014

In order to develop a new mineral exploration technique, a study was carried out about the potential mapping of Moisan area using SIP (Spectral Induced Polarization) data. The SIP inversion results were classified according to the geological regions, and the distribution characteristics of resistivity and phase values of SIP data were analyzed at the ore region. Based on the characteristics of SIP of ore bodies, we performed 3D potential mapping of Moisan area. The analyzed potential map was verified using that the locations and patterns of high potential regions of the results are well matched with those of the known ore bodies. If we get the higher spatial resolution SIP data, the potential mapping technique using SIP data can be effectively applied to the estimation of mining deposit.

• 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.

• Korean Journal of Remote Sensing
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• v.23 no.4
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• pp.287-295
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• 2007

A mineral potential map showing the distributions of potential areas for exploration of undiscovered mineral deposits is a kind of predictive thematic maps. For any predictive thematic maps to show reasonably significant prediction results, validation information on prediction capability should be provided in addition to spatial locations of high potential areas. The objective of this paper is to apply prediction rate curves to the estimation of prediction probability of future discovery. A case study for Au-Ag mineral potential mapping using geochemical data sets is carried out to illustrate procedures for estimating prediction probability and for an interpretation. Through the case study, quantitative information including prediction rates and probability obtained by prediction rate curves was found to be very important for the interpretation of prediction results. It is expected that such quantitative validation information would be effectively used as basic information for cost analysis of exploration and environmental impact assessment.

• Mineral and Industry
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• v.26
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• pp.32-36
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• 2013

• Journal of Soil and Groundwater Environment
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• v.28 no.6
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• pp.71-89
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• 2023

A groundwater potential map (GPM) was built for the Nakdonggang River Basin based on ten variables, including hydrogeologic unit, fault-line density, depth to groundwater, distance to surface water, lineament density, slope, stream drainage density, soil drainage, land cover, and annual rainfall. To integrate the thematic layers for GPM, the criteria were first weighted using the Analytic Hierarchical Process (AHP) and then overlaid using the Technique for Ordering Preferences by Similarity to Ideal Solution (TOPSIS) model. Finally, the groundwater potential was categorized into five classes (very high (VH), high (H), moderate (M), low (L), very low (VL)) and verified by examining the specific capacity of individual wells on each class. The wells in the area categorized as VH showed the highest median specific capacity (5.2 m³/day/m), while the wells with specific capacity < 1.39 m³/day/m were distributed in the areas categorized as L or VL. The accuracy of GPM generated in the work looked acceptable, although the specific capacity data were not enough to verify GPM in the studied large watershed. To create GPMs for the determination of high-yield well locations, the resolution and reliability of thematic maps should be improved. Criterion values for groundwater potential should be established when machine learning or statistical models are used in the GPM evaluation process.

• Journal of the Korean earth science society
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• v.35 no.6
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• pp.412-421
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• 2014

In order to develop an effective mineral exploration technique, this study was carried out about the potential mapping of Gagok mine. The deposit model of Gagok mine is widely known. Based on the deposit model, we constructed mining indicator indices using related igneous rocks, faults, and carbonate rocks. By analyzing the spatial correlation between ore and indicator index structures, we decided the weighting values of indices according to the distance from the index structure. The 3D potential mapping was performed using 3D geological model and geological indices. The analyzed potential map verified that the locations and patterns of high potential regions of the results were well matched with those of the known ore bodies. Using the potential mapping results, we could effectively predict the location of a high potential area that has similar geological settings with ore.

• Journal of Soil and Groundwater Environment
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• v.26 no.6
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• pp.1-17
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• 2021

Self-potential (SP) surveys measure naturally occurring differences in electrical potential in the absence of artificial sources and have been applied to various fields since the first application in mineral explorations. Among various causes of SP occurrences, streaming potential is generated by the flow of groundwater, and makes SP surveys suitable for the exploration of groundwater table fluctuation, fractures, sinkholes and landslide occurrences. Recently, there has been many studies that applied SP surveys to monitor water leakage through dikes and embankments. In this review paper, we first review the characteristics and theoretical backgrounds of streaming potential in saturated or unsaturated porous media to introduce it in the embankment among various application field. After the review of the background theory, we review the past cases of field SP surveys on dikes and embankments and also the characteristics of field streaming potential data in the surveys. Further, by analyzing past studies of qualitative as well as quantitative interpretation of SP survey data, we show the possibility of quantitative interpretation of streaming potential data obtained on dikes and embankments. Consequently, it is hope that this review paper helps researches on SP surveys on dikes and embankments, and provides basis for interpretation methods of the SP data to identify leaked area and further leakage rate (or permeability).