• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.383-383
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• 2022

기존 큰 지진을 대상으로 한 많은 연구결과들을 살펴보면, 큰 지진이 발생하기 전에 토양 속에 존재하는 다양한 인자들 중에 라돈(²²²Rn, 반감기=3.82일) 농도가 비정상적으로 증가하는 현상을 나타낸다. 이러한 결과들은 라돈발생의 경향성을 분석한다면, 지진발생에 대한 전조증상이나 예측이 가능함을 나타낸다. 본 연구에서는 포항지역을 중심으로 지진발생에 대한 전조증상이나 모니터링을 분석하기 위해 지하수 관측소에 설치된 라돈 관측기기에서 라돈 관측자료를 수집하고 이를 활용하는 연구를 수행하였다. 라돈 관측자료 기간은 2019년 11월부터 2020년 9월까지의 자료이며, 라돈인자 뿐만 아니라 지하수위, 강수량, 수온인자를 같이 분석하였으며, 동일기간 동안 발생한 진도 2 이상의 지진사례 6개(E1(M_L 3.5): 2019.12.30.; E2(M_L 3.2): 2020.01.30.; E3(M_L 2.4): 2020.02.09.; E4(M_L 2.7): 2020.02.16.; E5(M_L 2.8): 2020.05.27.; E6(M_L 2.1): 2020.09.22.)를 대상으로 하였다. 지진발생의 전조증상이 나타나는 지역을 분석하기 위해 Dobrovolsky radius values (Dobrovolsky et al., 1979)와 Harversine 관계식을 적용하였다. 적용결과, 시간적 분석에서 라돈의 증감 경향성이 지진발생의 전조증상과 유의미한 상관성이 있음을 확인하였으며, 공간적인 분석에서도 유의미하지는 않으나 상관성이 나타났다. 그 외 지하수위는 상관성이 어느 정도 나타났으나 강수량은 유의미한 결과를 나타내지는 않았다. 따라서 라돈을 활용한다면 지진발생의 전조증상을 시공간적으로 파악할 수 있음을 확인하였다.

• Journal of Environmental Policy
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• v.14 no.2
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• pp.149-170
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• 2015

The purpose of this paper is to figure out the research direction and information regarding Mt. Baekdu in Korea through analyses of the research field and trends. Firstly, we made inventory of journal papers, conference proceedings, and research reports published related to Mt. Baekdu. A total of 255 data, spanning the 34 years from the 1980s to the middle of 2014, were acquired and classified into categories according to the year, field, contents and study area. Results show that research on Mt. Baekdu has been performed more than twice since 2010 and study regarding prediction has been carried out in 54.7% cases. In addition, the importance of geo-spatial information is expected to increase in order to study Mt. Baekdu. Secondly, we made and analyzed a geospatial information using inventory of 234 detailed research contents in research reports by Korea Meteorological Administration (KMA) and National Institute of Meteorological Research (NIMR). Statistics on categories show that research regarding prediction accounted for 81.6% of cases and the study of geo-spatial information utilization accounted for 54.7% of cases. However, the focus on studying the Mt. Baekdu region accounted for only 20.1% of cases. Thus, this indicates that it is necessary to do research at Mt. Bakdu itself. If the directly available geo-spatial information system is developed related to Mt. Baekdu, it will save research costs and analysis time. This study can be used to manage information about the research field of Mt. Baekdu by analysing inventory of research references and geospatial information using inventories when the Mt. Baekdu area is the focus of future research.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1691-1707
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• 2022

Cheonji Lake, the caldera of Baekdu Mountain, located on the border of the Korean Peninsula and China, alternates between melting and freezing seasonally. There is a magma chamber beneath Cheonji, and variations in the magma chamber cause volcanic antecedents such as changes in the temperature and water pressure of hot spring water. Consequently, there is an abnormal region in Cheonji where ice melts quicker than in other areas, freezes late even during the freezing period, and has a high-temperature water surface. The abnormal area is a discharge region for hot spring water, and its ice gradient may be used to monitor volcanic activity. However, due to geographical, political and spatial issues, periodic observation of abnormal regions of Cheonji is limited. In this study, the degree of ice change in the optimal region was quantified using a Landsat -5/-7/-8 optical satellite image and a Modified U-Net regression model. From January 22, 1985 to December 8, 2020, the Visible and Near Infrared (VNIR) band of 83 Landsat images including anomalous regions was utilized. Using the relative spectral reflectance of water and ice in the VNIR band, unique data were generated for quantitative ice variability monitoring. To preserve as much information as possible from the visible and near-infrared bands, ice gradient was noticed by applying it to U-Net with two encoders, achieving good prediction accuracy with a Root Mean Square Error (RMSE) of 140 and a correlation value of 0.9968. Since the ice change value can be seen with high precision from Landsat images using Modified U-Net in the future may be utilized as one of the methods to monitor Baekdu Mountain's volcanic activity, and a more specific volcano monitoring system can be built.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.18 no.2_spc
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• pp.261-273
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• 2020

North Korea conducted the sixth underground nuclear test on September 3, 2017 at the Punggye-ri Nuclear Test Site (NTS). In contrast to the previous five nuclear tests, several induced earthquakes occurred around the NTS after the sixth nuclear test and this may have caused radioxenon leakages at the site. Considering these reported earthquakes, we performed atmospheric dispersion simulations on some radioxenon emission scenarios for this event using our Lagrangian Atmospheric Dose Assessment System (LADAS) model by employing the Unified Model (UM) based numerical weather prediction data produced by the Korea Meteorological Administration (KMA). To find out possible detection locations and times, we combined not only daily and weekly based delayed releases but also leakages after the reported earthquakes around the NTS to create emission scenarios. Our simulation results were generally in good agreement with the measured data of the Nuclear Safety and Security Commission and International Monitoring System (IMS) stations operated by the Comprehensive nuclear Test-Ban-Treaty Organization (CTBTO).

• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.2
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• pp.38-50
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• 2008

The site-specific seismic responses and corresponding seismic hazards are influenced mainly by the subsurface geologic and geotechnical dynamic characteristics. To estimate reliably the seismic responses in this study, a geotechnical information system (GTIS) within GIS framework was developed by introducing new concepts, which consist of the extended area containing the study area and the additional site visit for acquiring surface geo-knowledge data. The GIS-based GTIS was built for Gyeongju area, which has records of abundant historical seismic hazards reflecting the high potential of future earthquakes. At the study area, Gyeongju, intensive site investigations and pre-existing geotechnical data collections were performed and the site visits were additionally carried out for assessing geotechnical characteristics and shear wave velocity ($V_S$) representing dynamic property. Within the GTIS for Gyeongju area, the spatially distributed geotechnical layers and $V_S$ in the entire study area were reliably predicted from the site investigation data using the geostatistical kriging method. Based on the spatial geotechnical layers and $V_S$ predicted within the GTIS, a seismic zoning map on site period ($T_G$) from which the site-specific seismic responses according to the site effects can be estimated was created across the study area of Gyeongju. The spatial $T_G$ map at Gyeongju indicated seismic vulnerability of two- to five-storied buildings. In this study, the seismic zonation based on $T_G$ within the GIS-based GTIS was presented as regional efficient strategy for seismic hazard prediction and mitigation.

• Tunnel and Underground Space
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• v.27 no.6
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• pp.343-350
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• 2017

Amid increasingly saturated ground space, development of underground space has been booming throughout the world and excavation has been underway near the structure above or under the ground level. But the ground subsidence caused by improper or poor construction technologies, underground water leakage, sudden changes of stratum and the problem with earth retaining system component has been emerged as hot social issue. To deal with such problems nationwide, establishment of preventive and proactive disaster management and rapid restoration system has been pushed now. In this study, collection of the data on technology development trend to secure the underground safety was made, taking into account of internal change elements (changing groundwater level, damage to underground utilities, etc) and external change elements (vehicle load, earthquake and ground excavation, etc) during excavation. Amid the growing need of ground behavior analysis, ground subsidence evaluation technology, safe excavation to prevent ground subsidence and reinforcement technology, improvement of rapid restoration technology in preparation for ground subsidence and development of independent capability, this study is intended to introduce the technology development in a bid to prevent the ground subsidence during excavation. It's categorized into prediction/evaluation technology, complex detect technology, waterproof reinforcement technology, rapid restoration technology and excavation technology which, in part, has been in process now.

• Ecology and Resilient Infrastructure
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• v.8 no.4
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• pp.165-178
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• 2021

We studied driftwood behaviors including generation and deposition in a tsunami using a numerical simulation. We used an integrated two-dimensional numerical model, which included a driftwood dynamics model. The study area was Sendai, Japan. Observation data collected by Inagaki et al. (2012) were used to verify the simulation results by comparing them with driftwood deposition patterns. A simplified model was developed to consider the threshold of driftwood generation by the drag force of water flows. To consider the volume of driftwood generated, we estimated the total wood number in the study area using Google Earth. Therefore, we simulated more than 13,000 pieces of driftwood that were generated and transported inland from approximately 300,000 trees that were growing in the forest. The final distribution of the driftwood was similar to the observation data. The reproducibility of the generation and deposition patterns of driftwood showed good agreement in terms of longitudinal deposition pattern. In the future, a sensitivity analysis on driftwood parameters, such as the size of the wood, boundary conditions, and grid size, will be implemented to predict the travel patterns of driftwood. Such modeling will be a useful methodology for disaster prediction based on water flow and driftwood.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.32 no.2
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• pp.93-101
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• 2019

The objective of this study is to develop a model which can predict the seismic performance of the slope relatively accurately and efficiently by using artificial neural network(ANN) technique. The quantification of such the seismic performance of the slope is not easy task due to the randomness and the uncertainty of the earthquake input and slope model. Under these circumstances, probabilistic seismic fragility analyses of slope have been carried out by several researchers, and a closed-form equation for slope seismic performance was proposed through a multiple linear regression analysis. However, a traditional statistical linear regression analysis has shown a limit that cannot accurately represent the nonlinearistic relationship between the slope of various conditions and seismic performance. In order to overcome these problems, in this study, we attempted to apply the ANN to generate prediction models of the seismic performance of the slope. The validity of the derived model was verified by comparing this with the conventional multi-linear and multi-nonlinear regression models. As a result, the models obtained through the ANN basically showed excellent performance in predicting the seismic performance of the slope, compared to the models obtained by the statistical regression analyses of the previous study.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.159-165
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• 2023

While the frequency of seismic occurrence has been increasing recently, the domestic seismic response system is weak, the objective of this research is to compare and analyze the seismic vulnerability of buildings using statistical analysis and machine learning techniques. As the result of using statistical technique, the prediction accuracy of the developed model through the optimal scaling method showed about 87%. As the result of using machine learning technique, because the accuracy of Random Forest method is 94% in case of Train Set, 76.7% in case of Test Set, which is the highest accuracy among the 4 analyzed methods, Random Forest method was finally chosen. Therefore, Random Forest method was derived as the final machine learning technique. Accordingly, the statistical analysis technique showed higher accuracy of about 87%, whereas the machine learning technique showed the accuracy of about 76.7%. As the final result, among the 22,296 analyzed building data, the seismic vulnerabilities of 1,627(0.1%) buildings are expected as more dangerous when the statistical analysis technique is used, 10,146(49%) buildings showed the same rate, and the remaining 10,523(50%) buildings are expected as more dangerous when the machine learning technique is used. As the comparison of the results of using advanced machine learning techniques in addition to the existing statistical analysis techniques, in spatial analysis decisions, it is hoped that this research results help to prepare more reliable seismic countermeasures.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.177-188
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• 2022

Recently, the application of distributed acoustic sensors (DAS), which can replace geophones and seismometers, has significantly increased along with interest in micro-seismic monitoring technique, which is one of the CO₂ storage monitoring techniques. A significant amount of temporally and spatially continuous data is recorded in a DAS monitoring system, thereby necessitating fast and accurate data processing techniques. Because event detection and seismic phase picking are the most basic data processing techniques, they should be performed on all data. In this study, a machine learning-based P, S wave phase picking algorithm was developed to compensate for the limitations of conventional phase picking algorithms, and it was modified using a transfer learning technique for the application of DAS data consisting of a single component with a low signal-to-noise ratio. Our model was constructed by modifying the convolution-based EQTransformer, which performs well in phase picking, to the ResUNet structure. Not only the global earthquake dataset, STEAD but also the augmented dataset was used as training datasets to enhance the prediction performance on the unseen characteristics of the target dataset. The performance of the developed algorithm was verified using K-net and KiK-net data with characteristics different from the training data. Additionally, after modifying the trained model to suit DAS data using the transfer learning technique, the performance was verified by applying it to the DAS field data measured in the Pohang Janggi basin.