• Economic and Environmental Geology
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• v.56 no.4
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• pp.385-396
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• 2023

The Mesozoic Bansong Group, distributed along the NE-SW thrust fault zone of the Okcheon Fold Belt in the Danyang-Yeongwol-Jeongseon areas, contains important information on the two Mosozoic orogenic cycles in the Koran Peninsula, the Permian-Triassic Songrim Orogeny and the Jurassic Daebo Orogeny. This study aims to review previous studies on the stratigraphy, depositional period, and basin evolution of the Bansong Group and to suggest future research directions. The perspective on the implication of the Bansong Group in the context of the tectonic evolution of the Korean Peninsula is largely divided into two points of view. The traditional view assumes that it was deposited as a product of the post-collisional Songrim Orogeny and then subsequently deformed by the Daebo Orogeny. This interpretation is based on the stratigraphic, paleontologic, and structural geologic research carried out in the Danyang Coalfield area. On the other hand, recent research regards the Bansong Group as a product of syn-orogenic sedimentation during the Daebo Orogeny. This alternative view is based on the zircon U-Pb ages of pyroclastic rocks distributed in the Yeongwol area and their structural position. However, both models cannot comprehensively explain the paleontological and geochronological data derived from Bansong Group sediments. This suggests the need for a new basin evolution model integrated from multidisciplinary data obtained through sedimentology, structural geology, geochronology, petrology, and geochemistry studies.

• Economic and Environmental Geology
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• v.56 no.3
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• pp.331-341
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• 2023

When creating a soil contamination map using geostatistical techniques, there are various sources that can affect prediction errors. In this study, a grid-based soil contamination map was created from the sampling data of heavy metal concentrations in soil in abandoned mine areas using Ordinary Kriging. Five factors that were judged to affect the prediction error of the soil contamination map were selected, and the variation of the root mean squared error (RMSE) between the predicted value and the actual value was analyzed based on the Leave-one-out technique. Then, using a machine learning algorithm, derived the top three factors affecting the RMSE. As a result, it was analyzed that Variogram Model, Minimum Neighbors, and Anisotropy factors have the largest impact on RMSE in the Standard interpolation. For the variogram models, the Spherical model showed the lowest RMSE, while the Minimum Neighbors had the lowest value at 3 and then increased as the value increased. In the case of Anisotropy, it was found to be more appropriate not to consider anisotropy. In this study, through the combined use of geostatistics and machine learning, it was possible to create a highly reliable soil contamination map at the local scale, and to identify which factors have a significant impact when interpolating a small amount of soil heavy metal data.

• Economic and Environmental Geology
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• v.43 no.5
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• pp.467-475
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• 2010

The Landsat and ASTER data have been used in mineralogical and lithological studies, and they have also proved to be useful tool in the initial steps for mineral exploration throughout Nevada mining district, US. Huge pyrophyllite quarry mines, including Jungang, Samsung, Kyeongju, and Naenam located in the southeastern part of Gyeongsang Basin. The geology of study area consists mainly of Cretaceous volcanic rocks, which belong into Cretaceous Hayang and Jindong Group. They were intruded by Bulgugsa granites, so called Sannae-Eonyang granites. To extraction of Ratio model for pyrophyllite deposits, tuffaceous rock and pyrophyllite ores from the Jungang mine used in reflectance spectral analysis and these results were re-sampled to Landsat and ASTER bandpass. As a result of these processes, the pyrophyllite ores spectral features show strong reflectance at band 5, whereas strong absorption at band 7 in Landsat data. In the ASTER data, the pyrophyllite ores spectral features show strong absorption at band 5 and 8, whereas strong reflectance at band 4 and 7. Based on these spectral features, as a result of application of $Py_{Landsat}$ model to hydrothermal alteration zone and other exposed sites, the DN values of two different areas are 1.94 and 1.19 to 1.49, respectively. The differences values between pyrophyllite deposits and concrete-barren area are 0.472 and 0.399 for $Py_{ASTER}$ model, 0.452 and 0.371 for OHIb model, 0.365 and 0.311 for PAK model, respectively. Thus, $Py_{ASTER}$ and $Py_{Landsat}$ model proposed from this study proved to be more useful tool for the extraction of pyrophyllite deposits relative to previous models.