• Geophysics and Geophysical Exploration
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• v.26 no.2
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• pp.84-93
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• 2023

The supervised learning-based deep-learning seismic inversion techniques have demonstrated successful performance in synthetic data examples targeting small-scale areas. The supervised learning-based deep-learning seismic inversion uses time-domain wavefields as input and subsurface velocity models as output. Because the time-domain wavefields contain various types of wave information, the data size is considerably large. Therefore, research applying supervised learning-based deep-learning seismic inversion trained with a significant amount of field-scale data has not yet been conducted. In this study, we predict subsurface velocity models using Laplace-domain wavefields as input instead of time-domain wavefields to apply a supervised learning-based deep-learning seismic inversion technique to field-scale data. Using Laplace-domain wavefields instead of time-domain wavefields significantly reduces the size of the input data, thereby accelerating the neural network training, although the resolution of the results is reduced. Additionally, a large grid interval can be used to efficiently predict the velocity model of the field data size, and the results obtained can be used as the initial model for subsequent inversions. The neural network is trained using only synthetic data by generating a massive synthetic velocity model and Laplace-domain wavefields of the same size as the field-scale data. In addition, we adopt a towed-streamer acquisition geometry to simulate a marine seismic survey. Testing the trained network on numerical examples using the test data and a benchmark model yielded appropriate background velocity models.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.3
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• pp.321-335
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• 2023

Monitoring sediment loads in natural rivers is the key process in river engineering, but it is costly and dangerous. In practice, suspended loads are directly measured, and total loads, which is a summation of suspended loads and bed loads, are estimated. This study proposes a real-time sediment discharge monitoring system using the horizontal acoustic Doppler current profiler (H-ADCP) and support vector regression (SVR). The proposed system is comprised of the SVR model for suspended sediment concentration (SVR-SSC) and for total loads (SVR-Q_TL), respectively. SVR-SSC estimates SSC and SVR-Q_TL mimics the modified Einstein procedure. The grid search with K-fold cross validation (Grid-CV) and the recursive feature elimination (RFE) were employed to determine SVR's hyperparameters and input variables. The two SVR models showed reasonable cross-validation scores (R²) with 0.885 (SVR-SSC) and 0.860 (SVR-Q_TL). During the time-series sediment load monitoring period, we successfully detected various sediment transport phenomena in natural streams, such as hysteresis loops and sensitive sediment fluctuations. The newly proposed sediment monitoring system depends only on the gauged features by H-ADCP without additional assumptions in hydraulic variables (e.g., friction slope and suspended sediment size distribution). This method can be applied to any ADCP-installed discharge monitoring station economically and is expected to enhance temporal resolution in sediment monitoring.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.94-94
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• 2020

최근 정부의 신북방정책 추진에 따라 수자원분야에서는 동북아지역 국제 공유하천을 중심의 물 정보 및 연구협력 기회 확보와 지정학적 특성을 고려한 지역 현안해결 중심의 연구가 재조명 되고 있다. 두만강은 이러한 동북아의 중심에 위치하고 있으며, 중국, 북한, 러이사의 국경을 따라 흐르며 지역 수자원의 대부분을 공급하는 국제하천이다. 또한, 지난 2018년 5월에는 하구유역이 람사르(Ramsar) 습지로 승인됨에 따라 철새 등을 포함한 생태가치의 중요성도 크게 증가하였다. 하지만 이 지역은 유역의 지정학적 민감성과 접근이 제한된 관측 정보들로 인해 그 수자원·환경 효용성을 정확하게 파악할 수 없을 뿐만 아니라, 최근 기후변화에 따른 영향으로 홍수, 가뭄 등의 수재해와 수질오염 등의 문제가 발생하고 있어 가용한 기술기반의 직·간접적 접근을 통한 장기수문 및 환경변화 등에 대한 분석과 관리방안 수립 등의 연구가 필요하다. 본 연구에서는 이러한 미계측 두만강 유역을 대상으로 우선, 가용한 위성자료 및 광역지표모형(MERRA-2) 기반 NASA POWER(Prediction of Worldwide Energy Resource) 수문기상 자료와 SWAT(Soil and Water Assessment Tool) 모형을 활용하여 장기 수문영향을 평가하고자 한다. SWAT 모형은 전 지구적으로 활용 가능한 격자 해상도 약 30m의 위성기반 수치표고모형(DEM), 광역 토양도, 지역 토지이용도 자료를 활용하여 두만강 유역을 전체 19개 소유역 및 18개 하도, 138개 HRUs의 수문분석 단위로 구축하였으며, 모의는 미국 NOAA NCDC(National Climate Data Center) 및 중국 CMDC(China Meteorological Data Service Center)의 주요 관측지점에서 선별한 총 13개소의 위치에 대해 재분석된 기후/기상자료들(NASA POWER 강수, 기온, 풍속, 상대습도 및 일사량)을 적용, 1990년에서 2019년까지의 30개년도 연속자료를 구축활용 하였다. 한편, 모형의 검·보정은 앞서 언급한 관측 자료의 부재로 과거 문헌 등을 통해 파악할 수 있는 연 단위 수자원 총량 등을 활용해 진행코자한다. 아울러, 향후는 최근 활용 가능한 장기 위성관측 강수량을 적용, 재분석 자료 결과와의 비교를 통해 상호 분석 오류를 줄여나갈 수 있을 것으로도 판단된다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1B
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• pp.69-78
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• 2006

The rainfall depth-area-duration analysis which is used to characterize precipitation extremes for specification of so-called design storms, provides a basis for evaluation of drought severity when storm depth is replaced by an appropriate measure of drought severity. So we propose a method for constructing drought severity-area-duration curves in this study. Monthly precipitation data over the whole Korea are used to compute SPI. Such SPIs are abstracted to several independent spatial components from EOF analysis. Using Kriging method, these spatial components are used to constitute grid-based SPI data set over the whole Korea including Jeju island with $6km{\times}6km$ resolution. After identifying main drought events, the drought severity-area-duration curves for these events over 32-year period of record are finally constructed. As a result, such curves show the similar shape with storm-based curves in the sense that the drought severity (or rainfall depth) is inversely proportional to drought area from the curves, but drought-based curves are different from storm-based curves in the sense that the drought severity decreasing rate with respect to drought area is much less than depth decreasing rate.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.404-414
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• 2023

In this study, digital climate maps with high-resolution (1km, daily) for the period of 1981 to 2020 were produced for the use as reference data within the procedures for statistical downscaling of climate change scenarios. Grid data for the six climate variables including maximum temperature, minimum temperature, precipitation, wind speed, relative humidity, solar radiation was created over Korean Peninsula using statistical downscaling model, so-called IGISRM (Improved GIS-based Regression Model), using global reanalysis data and in-situ observation. The digital climate data reflects topographical effects well in terms of representing general behaviors of observation. In terms of Correlation Coefficient, Slope of scatter plot, and Normalized Root Mean Square Error, temperature-related variables showed satisfactory performance while the other variables showed relatively lower reproducibility performance. These digital climate maps based on observation will be used to downscale future climate change scenario data as well as to get the information of gridded agricultural weather data over the whole Korean Peninsula including North Korea.

• Journal of Korea Water Resources Association
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• v.57 no.4
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• pp.289-300
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• 2024

The purpose of this study is to evaluate the applicability of the GRM (Grid based rainfall-Runoff Model) to the continuous simulation by simulating the dam inflow. The GRM was previously developed for the simulation of rainfall-runoff events but has recently been improved to enable continuous simulation. The target watersheds are Chungju dam, Andong dam, Yongdam dam, and Sumjingang dam basins, and runoff models were constructed with the spatial resolution of 500 m × 500 m. The simulation period is 21 years (2001 to 2021). The simulation results were evaluated over the 17 year period (2005 to 2021), and were divided into three data periods: total duration, wet season (June to September), and dry season (October to May), and compared with the observed daily inflow of each dam. Nash-Sutcliffe efficiency (NSE), Kling-Gupta efficiency (KGE), correlation coefficient (CC), and total volume error (VE) were used to evaluate the fitness of the simulation results. As a result of evaluating the simulated dam inflow, the observed data could be well reproduced in the total duration and wet season, and the dry season also showed good simulation results considering the uncertainty of low-flow data. As a result of the study, it was found that the continuous simulation technique of the GRM model was properly implemented and the model was sufficiently applicable to the simulation of dam inflow in this study.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.3
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• pp.164-178
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• 2022

Uncertainties in weather forecasts would affect the reliability of yield prediction using crop models. The objective of this study was to compare uncertainty in crop yield prediction caused by the use of the weather forecast data. Daily weather data were produced at 10 km spatial resolution using W eather Research and Forecasting (W RF) model. The nearest neighbor method was used to downscale these data at the resolution of 5 km (W RF5K). Parameter-elevation Regressions on Independent Slopes Model (PRISM) was also applied to the WRF data to produce the weather data at the same resolution. W RF5K and PRISM data were used as inputs to the CROPGRO-SOYBEAN model to predict crop yield. The uncertainties of the gridded data were analyzed using cumulative growing degree days (CGDD) and cumulative solar radiation (CSRAD) during the soybean growing seasons for the crop of interest. The degree of agreement (DOA) statistics including structural similarity index were determined for the crop model outputs. Our results indicated that the DOA statistics for CGDD were correlated with that for the maturity dates predicted using WRF5K and PRISM data. Yield forecasts had small values of the DOA statistics when large spatial disagreement occured between maturity dates predicted using WRF5K and PRISM. These results suggest that the spatial uncertainties in temperature data would affect the reliability of the phenology and, as a result, yield predictions at a greater degree than those in solar radiation data. This merits further studies to assess the uncertainties of crop yield forecasts using a wide range of crop calendars.

• Korean Journal of Computational Design and Engineering
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• v.17 no.1
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• pp.45-53
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• 2012

In this paper, oil spreading simulation model is proposed for analyzing the oil spreading phenomenon rapidly when the ocean is polluted by the oil from a stranded ship. The space occupied by the ocean is converted into the latticed cell, and the each cell contains the information, such as the quantity of the oil, the temperature of the ocean, and the direction of current and wind. Two states, such as "clean" and "polluted" are defined in the each cell, and the oil in the cell spreads to the neighbor cells by the spreading rules. There are three spreading rules. First, the oil in the certain cell only spreads to the neighbor cells that contain larger oil than the certain cell. Second, the oil evaporates in proportion to the temperature of the ocean at the every time step. Third, the oil spreading property is affected by the direction and the speed of the current and the wind. The oil spreading simulation model of the each cell is defined by using the combined discrete event and discrete time simulation model architecture with the information and the spreading rules in the cell. The oil spreading simulation is performed when the oil of 10,000 kL is polluted in the ocean environment of 300 m by 300 m with various current and wind.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.5
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• pp.39-48
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• 2009

Many super-resolution reconstruction algorithms have been proposed since it was the first proposed in 1984. The spatial domain approach of the super-resolution reconstruction methods is accomplished by mapping the low resolution image pixels into the high resolution image pixels. Generally, a super-resolution reconstruction algorithm by using the spatial domain approach has the noise problem because the low resolution images have different noise component, different PSF, and distortion, etc. In this paper, we proposed the new super-resolution reconstruction method that uses the L1 norm to minimize noise source and also uses the Huber norm to preserve edges of image. The proposed algorithm obtained the higher image quality of the result high resolution image comparing with other algorithms by experiment.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.354-354
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• 2011

금강유역에 대한 기후변화 영향을 기후모형 및 수자원영향평가모형을 통하여 정량적으로 분석하기 위하여 한반도 최적 GCM모형으로 기상청에서 제공하는 ECHO-G GCM모형과 역학적 다운스케일링 기법을 이용한 공간해상도 27km의 지역규모의 MM5 RCM모형을 이용하였다. A1B시나리오 기반으로 고해상도 기후변화 시나리오를 작성하여 분석기간을 2015년대(2001-2030), 2045년대(2031-2060), 2075년대(2061-2090)로 구분하여 미래 연평균강수량, 기온 등을 전망하였고, 과거 30년 자료의 100년빈도 강수량과 미래의 100년 빈도강수량의 변동성을 평가하였다. 기본적으로 GCM 및 RCM은 시공간적 스케일의 상이성으로 인해 수자원 영향 평가를 위한 자료로서 직접적인 이용은 현실적으로 곤란하다는 점에서 본 연구에서는 RCM 격자자료를 유역단위에서 강우관측소 지점 단위로 공간적 Downscaling을 실시하였으며 RCM 월자료에 대해서 일단위 자료로 시간적 Downscaling을 수행하여 기후모델로부터 발생하는 시공간적 스케일의 문제점을 극복하였다. 또한 유역단위의 상세수문시나리오를 생산하기 위해서 다지점 비정상성 Downscaling 기법을 활용하여 기존 일강수량 모의기법에서 간과 되었던 비정상성을 고려하여 미래 기후변화에 따른 강수사상의 변동성을 다양한 방법으로 검토하였다. 2001년~2006년 기간동안 SWAT모형을 이용하여 용담댐유역 용담댐 지점의 유입량과 SWAT의 최종방류부의 유량분석값을 비교한 결과 모의치와 실측치가 90.1% 일치하는 것으로 나타났고, 천천수위관측소 지점의 유량을 모형결과와 비교분석 한 결과에서도 91.3% 일치하는 것으로 나타났다. 한편, 대청댐 지점의 유입량과 SWAT의 최종방류부의 유량분석값을 비교한 결과 모의치와 실측치가 84.4% 일치하는 것으로 나타나 금강유역내 용담댐 및 대청댐을 대상으로 유출분석 검토 결과 적용성이 있음을 확인하였다. 기후변화 분석기간은 2011년부터 2090년까지 80년을 대상기간 으로 선정하였으며, 분석결과 2011~2020년 사이 유출량이 18%증가하는 것으로 전망되었다.