• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.6 no.4
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• pp.307-327
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• 2008

Geochemical study on the rocks and minerals of the Gyeongju low and intermediate level waste repository was carried out in order to provide geochemical data for the safety assessment and geochemical modeling. Polarized microscopy, X-ray diffraction method, chemical analysis for the major and trace elements, scanning electron microscopy(SEM), and stable isotope analysis were applied. Fracture zones are locally developed with various degrees of alteration in the study area. The study area is mainly composed of granodiorite and diorite and their relation is gradational in the field. However, they could be easily distinguished by their chemical property. The granodiorite showed higher $SiO_2$ content and lower MgO and $Fe_2O_3$ contents than the diorite. Variation trends of the major elements of the granodiorite and diorite were plotted on the same line according to the increase of $SiO_2$ content suggesting that they were differentiated from the same magma. Spatial distribution of the various elements showed that the diorite region had lower $SiO_2,\;Al_2O_3,\;Na_2O\;and\;K_2O$ contents, and higher CaO, $Fe_2O_3$ contents than the granodiorite region. Especially, because the differences in the CaO and $Na_2O$ distribution were most distinct and their trends were reciprocal, the chemical variation of the plagioclase of the granitic rocks was the main parameter of the chemical variation of the host rocks in the study area. Identified fracture-filling minerals from the drill core were montmorillonite, zeolite minerals, chlorite, illite, calcite and pyrite. Especially pyrite and laumontite, which are known as indicating minerals of hydrothermal alteration, were widely distributed in the study area indicating that the study area was affected by mineralization and/or hydrothermal alteration. Sulfur isotope analysis for the pyrite and oxygen-hydrogen stable isotope analysis for the clay minerals indicated that they were originated from the magma. Therefore, it is considered that the fracture-filling minerals from the study area were affected by the hydrothermal solution as well as the simply water-rock interaction.

• Economic and Environmental Geology
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• v.44 no.3
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• pp.229-238
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• 2011

S-wave, which provides lithology and pore fluid information, plays a key role in estimating gas-hydrate saturation. In general, P- and S-wave velocities increase in the presence of gas-hydrate and the P-wave velocity decreases in the presence of free gas under the gas-hydrate layer. Whereas there are very small changes, even slightly increases, in the S-wave velocity in the free gas layer because S-wave is not affected by the pore fluid when propagating in the free gas layer. To verify those velocity properties of the BSR (bottom-simulating reflector) depth in the gas-hydrate prospect area in the Ulleung Basin, P- and S-wave velocity profiles were derived from multi-component ocean-bottom seismic data which were acquired by Korea Institute of Geoscience and Mineral Resources (KIGAM) in May 2009. OBS (ocean-bottom seismometer) hydrophone component data were modeled and inverted first through the traveltime inversion method to derive P-wave velocity and depth model of survey area. 2-D multichannel stacked data were incorporated as an initial model. Two horizontal geophone component data, then, were polarization filtered and rotated to make radial component section. Traveltimes of main S-wave events were picked and used for forward modeling incorporating Poisson's ratio. This modeling provides S-wave profiles and Poisson's ratio profiles at every OBS site. The results shows that P-wave velocities in most OBS sites decrease beneath the BSR, whereas S-wave velocities slightly increase. Consequently, Poisson's ratio decreased strongly beneath the BSR indicating the presence of a free gas layer under the BSR.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1723-1735
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• 2022

Landslides are one of the most prevalent natural disasters, threating both humans and property. Also landslides can cause damage at the national level, so effective prediction and prevention are essential. Research to produce a landslide susceptibility map with high accuracy is steadily being conducted, and various models have been applied to landslide susceptibility analysis. Pixel-based machine learning models such as frequency ratio models, logistic regression models, ensembles models, and Artificial Neural Networks have been mainly applied. Recent studies have shown that the kernel-based convolutional neural network (CNN) technique is effective and that the spatial characteristics of input data have a significant effect on the accuracy of landslide susceptibility mapping. For this reason, the purpose of this study is to analyze landslide vulnerability using a pixel-based deep neural network model and a patch-based convolutional neural network model. The research area was set up in Gangwon-do, including Inje, Gangneung, and Pyeongchang, where landslides occurred frequently and damaged. Landslide-related factors include slope, curvature, stream power index (SPI), topographic wetness index (TWI), topographic position index (TPI), timber diameter, timber age, lithology, land use, soil depth, soil parent material, lineament density, fault density, normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used. Landslide-related factors were built into a spatial database through data preprocessing, and landslide susceptibility map was predicted using deep neural network (DNN) and CNN models. The model and landslide susceptibility map were verified through average precision (AP) and root mean square errors (RMSE), and as a result of the verification, the patch-based CNN model showed 3.4% improved performance compared to the pixel-based DNN model. The results of this study can be used to predict landslides and are expected to serve as a scientific basis for establishing land use policies and landslide management policies.