• 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.

• Journal of Korea Water Resources Association
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• v.46 no.8
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• pp.833-842
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• 2013

The quantile mapping is utilized to reproduce reliable GCM(Global Climate Model) data by correct systematic biases included in the original data set. This scheme, in general, projects the Cumulative Distribution Function (CDF) of the underlying data set into the target CDF assuming that parameters of target distribution function is stationary. Therefore, the application of stationary quantile mapping for nonstationary long-term time series data of future precipitation scenario computed by GCM can show biased projection. In this research the Nonstationary Quantile Mapping (NSQM) scheme was suggested for bias correction of nonstationary long-term time series data. The proposed scheme uses the statistical parameters with nonstationary long-term trends. The Gamma distribution was assumed for the object and target probability distribution. As the climate change scenario, the 20C3M(baseline scenario) and SRES A2 scenario (projection scenario) of CGCM3.1/T63 model from CCCma (Canadian Centre for Climate modeling and analysis) were utilized. The precipitation data were collected from 10 rain gauge stations in the Han-river basin. In order to consider seasonal characteristics, the study was performed separately for the flood (June~October) and nonflood (November~May) seasons. The periods for baseline and projection scenario were set as 1973~2000 and 2011~2100, respectively. This study evaluated the performance of NSQM by experimenting various ways of setting parameters of target distribution. The projection scenarios were shown for 3 different periods of FF scenario (Foreseeable Future Scenario, 2011~2040 yr), MF scenario (Mid-term Future Scenario, 2041~2070 yr), LF scenario (Long-term Future Scenario, 2071~2100 yr). The trend test for the annual precipitation projection using NSQM shows 330.1 mm (25.2%), 564.5 mm (43.1%), and 634.3 mm (48.5%) increase for FF, MF, and LF scenarios, respectively. The application of stationary scheme shows overestimated projection for FF scenario and underestimated projection for LF scenario. This problem could be improved by applying nonstationary quantile mapping.

• The Journal of the Petrological Society of Korea
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• v.22 no.2
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• pp.137-151
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• 2013

This study is focused on element behaviors and mineral compositions of the fault rock developed in Yongdang-ri, Yangbuk-myeon, Gyeongju City, Korea, using XRF, ICP, XRD, and EPMA/BSE in order to better understand the chemical variations in fault rocks during the fault activity, with emphasis on dependence of chemical mobility on mineralogy across the fault zone. As one of the main components of the fault rocks, $SiO_2$ shows the highest content which ranges from 61.6 to 71.0%, and $Al_2O_3$ is also high as having the 10.8~15.8% range. Alkali elements such as $Na_2O$ and $K_2O$ are in the range of 0.22~4.63% and 2.02~4.89%, respectively, and $Fe_2O_3$ is 3.80~12.5%, indicating that there are significant variations within the fault rock. Based on the chemical characteristics in the fault rocks, it is evident that the fault gouge zone is depleted in $Na_2O$, $Al_2O_3$, $K_2O$, $SiO_2$, CaO, Ba and Sr, whereas enriched in $Fe_2O_3$, MgO, MnO, Zr, Hf and Rb relative to the fault breccia zone. Such chemical behaviors are closely related to the difference in the mineral compositions between breccia and gouge zones because the breccia zone consists of the rock-forming minerals including quartz and feldspar, whereas the gouge zone consists of abundant clay minerals such as illite and chlorite. The alteration of the primary minerals leading to the formation of the clay minerals in the fault zone was affected by the hydrothermal fluids involved in fault activity. Taking into account the fact that major, trace and rare earth elements were leached out from the precursor minerals, it is assumed that the element mobility was high during the first stage of the fault activity because the fracture zone is interpreted to have acted as a path of hydrothermal fluids. Moving toward the later stage of fault activity, the center of the fracture zone was transformed into the gouge zone during which the permeability in the fault zone gradually decreased with the formation of clay minerals. Consequently, elements were effectively constrained in the gouge zone mostly filled with authigenic minerals including clay minerals, characterized by the low element mobility.

• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1159-1172
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• 2020

This study applied deep convolution neural network based on U-Net and SegNet using long period weather radar data to very short-term rainfall prediction. And the results were compared and evaluated with the translation model. For training and validation of deep neural network, Mt. Gwanak and Mt. Gwangdeoksan radar data were collected from 2010 to 2016 and converted to a gray-scale image file in an HDF5 format with a 1km spatial resolution. The deep neural network model was trained to predict precipitation after 10 minutes by using the four consecutive radar image data, and the recursive method of repeating forecasts was applied to carry out lead time 60 minutes with the pretrained deep neural network model. To evaluate the performance of deep neural network prediction model, 24 rain cases in 2017 were forecast for rainfall up to 60 minutes in advance. As a result of evaluating the predicted performance by calculating the mean absolute error (MAE) and critical success index (CSI) at the threshold of 0.1, 1, and 5 mm/hr, the deep neural network model showed better performance in the case of rainfall threshold of 0.1, 1 mm/hr in terms of MAE, and showed better performance than the translation model for lead time 50 minutes in terms of CSI. In particular, although the deep neural network prediction model performed generally better than the translation model for weak rainfall of 5 mm/hr or less, the deep neural network prediction model had limitations in predicting distinct precipitation characteristics of high intensity as a result of the evaluation of threshold of 5 mm/hr. The longer lead time, the spatial smoothness increase with lead time thereby reducing the accuracy of rainfall prediction The translation model turned out to be superior in predicting the exceedance of higher intensity thresholds (> 5 mm/hr) because it preserves distinct precipitation characteristics, but the rainfall position tends to shift incorrectly. This study are expected to be helpful for the improvement of radar rainfall prediction model using deep neural networks in the future. In addition, the massive weather radar data established in this study will be provided through open repositories for future use in subsequent studies.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.233-246
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• 2023

In terms of understanding the water cycle and efficient water resource management, the importance of soil moisture has been highlighted. However, in Korea, the lack of qualified in-situ soil moisture data results in very limited utility. Even if satellite-based data are applied, the absence of ground reference data makes objective evaluation and correction difficult. The cosmic-ray neutron probe (CRNP) can play a key role in producing data for satellite data calibration. The installation of CRNP is non-invasive, minimizing damage to the soil and vegetation environment, and has the advantage of having a spatial representative for the intermediate scale. These characteristics are advantageous to establish an observation network in Korea which has lots of mountainous areas with dense vegetation. Therefore, this study was conducted to evaluate the applicability of the CRNP soil moisture observatory in Korea as part of the establishment of a Korean cOsmic-ray Soil Moisture Observing System (KOSMOS). The CRNP observation station was installed with the Gunup-ri observation station, considering the ease of securing power and installation sites and the efficient use of other hydro-meteorological factors. In order to evaluate the CRNP soil moisture data, 12 additional in-situ soil moisture sensors were installed, and spatial representativeness was evaluated through a temporal stability analysis. The neutrons generated by CRNP were found to be about 1,087 counts per hour on average, which was lower than that of the Solmacheon observation station, indicating that the Hongcheon observation station has a more humid environment. Soil moisture was estimated through neutron correction and early-stage calibration of the observed neutron data. The CRNP soil moisture data showed a high correlation with r=0.82 and high accuracy with root mean square error=0.02 m³/m³ in validation with in-situ data, even in a short calibration period. It is expected that higher quality soil moisture data production with greater accuracy will be possible after recalibration with the accumulation of annual data reflecting seasonal patterns. These results, together with previous studies that verified the excellence of CRNP soil moisture data, suggest that high-quality soil moisture data can be produced when constructing KOSMOS.