• Economic and Environmental Geology
• /
• v.52 no.6
• /
• pp.573-586
• /
• 2019

A seismic hazard map based on spatial analysis of various sources of geologic seismic information was developed and assessed for regional seismic vulnerability in South Korea. The indicators for assessment were selected in consideration of the geological characteristics affecting the seismic damage. Probabilistic seismic hazard and fault information were used to be associated with the seismic activity hazard and bedrock depth related with the seismic damage hazard was also included. Each indicator was constructed of spatial information using GIS and geostatistical techniques such as ordinary kriging, line density mapping and simple kriging with local varying means. Three spatial information constructed were integrated by assigning weights according to the research purpose, data resolution and accuracy. In the case of probabilistic seismic hazard and fault line density, since the data uncertainty was relatively high, only the trend was intended to be reflected firstly. Finally, the seismic activity hazard was calculated and then integrated with the bedrock depth distribution as seismic damage hazard indicator. As a result, a seismic hazard map was proposed based on the analysis of three spatial data and the southeast and northwest regions of South Korea were assessed as having high seismic hazard. The results of this study are expected to be used as basic data for constructing seismic risk management systems to minimize earthquake disasters.

• Journal of Korea Water Resources Association
• /
• v.55 no.12
• /
• pp.1137-1147
• /
• 2022

River and groundwater stages are the main elements in the hydrologic cycle. They are spatially correlated and can be used to evaluate hydrological and agricultural drought. Stochastic simulation is often performed independently on hydrological variables that are spatiotemporally correlated. In this setting, interdependency across mutual variables may not be maintained. This study proposes the Bayesian vector autoregression model (VAR) to capture the interdependency between multiple variables over time. VAR models systematically consider the lagged stages of each variable and the lagged values of the other variables. Further, an autoregressive model (AR) was built and compared with the VAR model. It was confirmed that the VAR model was more effective in reproducing observed interdependency (or cross-correlation) between river and ground stages, while the AR generally underestimated that of the observed.

• Progress in Medical Physics
• /
• v.23 no.2
• /
• pp.81-90
• /
• 2012

The effect of setup uncertainties on CTV dose and the correlation between setup uncertainties and setup margin were evaluated by Monte Carlo based numerical simulation. Patient specific information of IMRT treatment plan for rectal cancer designed on the VARIAN Eclipse planning system was utilized for the Monte Carlo simulation program including the planned dose distribution and tumor volume information of a rectal cancer patient. The simulation program was developed for the purpose of the study on Linux environment using open source packages, GNU C++ and ROOT data analysis framework. All misalignments of patient setup were assumed to follow the central limit theorem. Thus systematic and random errors were generated according to the gaussian statistics with a given standard deviation as simulation input parameter. After the setup error simulations, the change of dose in CTV volume was analyzed with the simulation result. In order to verify the conventional margin recipe, the correlation between setup error and setup margin was compared with the margin formula developed on three dimensional conformal radiation therapy. The simulation was performed total 2,000 times for each simulation input of systematic and random errors independently. The size of standard deviation for generating patient setup errors was changed from 1 mm to 10 mm with 1 mm step. In case for the systematic error the minimum dose on CTV $D_{min}^{stat{\cdot}}$ was decreased from 100.4 to 72.50% and the mean dose $\bar{D}_{syst{\cdot}}$ was decreased from 100.45% to 97.88%. However the standard deviation of dose distribution in CTV volume was increased from 0.02% to 3.33%. The effect of random error gave the same result of a reduction of mean and minimum dose to CTV volume. It was found that the minimum dose on CTV volume $D_{min}^{rand{\cdot}}$ was reduced from 100.45% to 94.80% and the mean dose to CTV $\bar{D}_{rand{\cdot}}$ was decreased from 100.46% to 97.87%. Like systematic error, the standard deviation of CTV dose ${\Delta}D_{rand}$ was increased from 0.01% to 0.63%. After calculating a size of margin for each systematic and random error the "population ratio" was introduced and applied to verify margin recipe. It was found that the conventional margin formula satisfy margin object on IMRT treatment for rectal cancer. It is considered that the developed Monte-carlo based simulation program might be useful to study for patient setup error and dose coverage in CTV volume due to variations of margin size and setup error.