• 한국지구물리탐사학회:학술대회논문집
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• 2003.11a
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• pp.352-361
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• 2003

We have evaluated the extent of saltwater intrusion from electrical resistivity distribution in a coastal aquifer system in the southeastern part of Busan, Korea. This aquifer system is divided into four layers according to the hydrogeologic characteristics and the horizontal extent of intruded saltwater is determined at each layer through the geostatistical interpretation of electrical resistivity data. In order to define the statistical structure of electrical resistivity data, variogram analysis is carried out to obtain best generalized covariance models. IRF-k (intrinsic random function of order k) kriging is performed with covariance models to produce the plane of spatial mean resistivities. The kriged estimates are evaluated by cross validation to show a good agreement with the true values and the statistics of cross validation represented low errors for the estimates. In the resistivity contour maps more than 5 m below the surface, we can see a dominant direction of saltwater intrusion beginning from the east side. The area of saltwater intrusion increases with depth. The northeast side has low resistivities less than 5 ohm-m due to the presence of saline water in the depth range of 20 m through 70 m. These results show that the application of geostatistical technique to electrical resistivity data is useful for assessing saltwater intrusion in a coastal aquifer system.

• Journal of Environmental Science International
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• v.27 no.7
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• pp.509-518
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• 2018

Weather is the most influential factor for crop cultivation. Weather information for cultivated areas is necessary for growth and production forecasting of agricultural crops. However, there are limitations in the meteorological observations in cultivated areas because weather equipment is not installed. This study tested methods of predicting the daily mean temperature in onion fields using geostatistical models. Three models were considered: inverse distance weight method, generalized additive model, and Bayesian spatial linear model. Data were collected from the AWS (automatic weather system), ASOS (automated synoptic observing system), and an agricultural weather station between 2013 and 2016. To evaluate the prediction performance, data from AWS and ASOS were used as the modeling data, and data from the agricultural weather station were used as the validation data. It was found that the Bayesian spatial linear regression performed better than other models. Consequently, high-resolution maps of the daily mean temperature of Jeonnam were generated using all observed weather information.

• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.1
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• pp.168-178
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• 2006

In recent years, an ingress of mass jellyfish into cooling water intake system causes interruption of electric power production at the Uljin nuclear power plant. Therefore, monitering and forecast on the mass ingress of marine organisms are demanded as one of the early preventing measurements. Sea water movement is a major factor on the ingress of marine organisms like Moon jellyfish which has weak self-mobile ability. When sea surface flow direction adjacent to the Uljin is the northwest, the jellyfish on the Tsushima warm currents move to the Uljin power plant. To detect the direction of sea surface warm flows, the spatial range with $25km{\times}25km$ is set up and NOAA sea surface temperature(SST) data are collected in this area. For the statistical analysis, the SST data are made as GIS point data and geostatistical analysis of ArcGIS is used. Analyzing directional semivariogram, the anisotropy of the SST point data are calculated and warm flow direction is detected. This experimental results are expected to use as an element technology for the early warning system development of mass jellyfish ingress in power plant.

• Korean Journal of Remote Sensing
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• v.29 no.1
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• pp.69-79
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• 2013

This paper presents a two-stage geostatistical integration approach that aims at downscaling of coarse scale remote sensing data. First, downscaling of the coarse scale sedoncary data is implemented using area-to-point kriging, and this result will be used as trend components on the next integration stage. Then simple kriging with local varying means that integrates sparse precise observation data with the downscaled data is applied to generate thematic information at a finer scale. The presented approach can not only account for the statistical relationships between precise observation and secondary data acquired at the different scales, but also to calibrate the errors in the secondary data through the integration with precise observation data. An experiment for precipitation mapping with weather station data and TRMM (Tropical Rainfall Measuring Mission) data acquired at a coarse scale is carried out to illustrate the applicability of the presented approach. From the experiment, the geostatistical downscaling approach applied in this paper could generate detailed thematic information at various finer target scales that reproduced the original TRMM precipitation values when upscaled. And the integration of the downscaled secondary information with precise observation data showed better prediction capability than that of a conventional univariate kriging algorithm. Thus, it is expected that the presented approach would be effectively used for downscaling of coarse scale data with various data acquired at different scales.

• Journal of the Korean earth science society
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• v.35 no.1
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• pp.29-40
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• 2014

A DC resistivity survey was performed to detect anomalies beneath concrete pavement. A set of high conductive media and planar electrodes were used to lessen the effect's a high contact resistance of concrete. Results of the resistivity survey were analyzed and compared with those of other geophysical surveys such as Ground Penetration Radar (GPR), Impulse Response (IR), and Multi-channel Analysis of Surface Waves (MASW), which were carried out in the same location. The results of resistivity survey showed a high resistive distribution in the section of sink and pavement where a pattern of reinforcement was observed through the GPR survey. Also, a comparison of results between the IR and resistivity surveys indicated that the high resistivity was produced by the high dynamic stiffness in the reinforced section. The co-Kriging of both the results of DC resistivity and MASW surveys at the same location showed that an integrated geostatistical analysis is able to give more accurate description on the anomalous subsurface region than can a separate analysis of each geophysical approach. This study suggests that the integrated geostatistical approaches were used for a decision-making process based on the geophysical surveys.

• Journal of the Korean earth science society
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• v.32 no.6
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• pp.613-628
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• 2011

Restoration, prediction and noise analysis of geomagnetic data measured in the Korean Peninsula were performed. Restoration methods based on an optimized principal component analysis (PCA) and the geostatistical kriging approach were proposed, and its effectiveness was also interpreted. The PCA-based method seemed to be effective to restore the periodical signals and the geostatistical approach was stable to fill the gaps of measurements. To analyze the noise level for each observatory, the geomagnetic time-series was plotted by scattergram which reflects the spatial variation, using data observed during same period. The scattergram showed that the observation made at Cheongyang seemed to have better quality in spatial continuity and stability, and the restoration result was also better than that of Icheon site. For the restoration, both of the methods, geostatistical and optimizaed PCA, showed stable result when the missing of observation was within 20 points. However, in case of more missing observations than 20 points and prediction problem, the optimized PCA seemed to be closer to the real observation considering the frequency-domain characteristics. The prediction using the optimized PCA seems to be plausible for one day of period for interpretation.

• Geophysics and Geophysical Exploration
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• v.10 no.4
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• pp.252-258
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• 2007

We have studied the feasibility of geostatistics approach to enhancing analysis of sparsely obtained seismic data by combining with satellite gravity data. The shallow depth and numerous fishing nets in The Yellow Sea, west of Korea, makes it difficult to do seismic surveys in this area. Therefore, we have attempted to use geostatistics to integrate the seismic data along with gravity data. To evaluate the feasibility of this approach, we have extracted only a few seismic profile data from previous surveys in the Yellow Sea and performed integrated analysis combining with the results from gravity data under the assumption that seismic velocity and density have a high physical correlation. First, we analyzed the correlation between extracted seismic profiles and depths obtained from gravity inversion. Next, we transferred the gravity depth to travel time using non-linear indicator transform and analyze residual values by kriging with varying local means. Finally, the reconstructed time structure map was compared with the original seismic section given in the previous study. Our geostatistical approach demonstrates relatively satisfactory results and especially, in the boundary area where seismic lines are sparse, gives us more in-depth information than previously available.

• Economic and Environmental Geology
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• v.50 no.3
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• pp.225-238
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• 2017

In this study, two viable measures of mean length and cumulative thickness of sand layers as important spatial statistics responsible for optimal SAGD (Steam Assisted Gravity Drainage) location for oil sand development were compared. For the comparisons, various deposits composed of sand and clay media were realized using a geostatistical simulator and the extent of steam chamber is simulated using multiphysics numerical simulator (dualphase flow and heat transfer). Based on the spatial statistics of each realization and the corresponding size of simulated steam chamber, the representativeness of two candidate measures (cumulative thickness and mean length of permeable media) were compared. The results of the geostatistical and SAGD simulations suggest that the mean length of permeable media is better correlated to the size of steam chamber than the cumulative thickness. Given those two-dimensional results, it is concluded that the cumulative thickness of the permeable media alone may not be a sufficient criterion for determining an optimal SAGD location and the mean length needs to be complementarily considered for the sound selections.

• Journal of The Geomorphological Association of Korea
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• v.20 no.4
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• pp.85-99
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• 2013

The objective of this paper is to analyze the spatial distribution of errors in the DEM generated using waterlines from multi-temporal remote sensing data and to assess flood vulnerability. Unlike conventional research in which only global statistics of errors have been generated, this paper tries to quantitatively analyze the spatial distribution of errors from a probabilistic viewpoint using geostatistical simulation. The initial DEM in Baramarae tidal flats was generated by corrected tidal level values and waterlines extracted from multi-temporal Landsat data in 2010s. When compared with the ground measurement height data, overall the waterline-based DEM underestimated the actual heights and local variations of the errors were observed. By applying sequential Gaussian simulation based on spatial autocorrelation of DEM errors, multiple alternative error distributions were generated. After correcting errors in the initial DEM with simulated error distributions, probabilities for flood vulnerability were estimated under the sea level rise scenarios of IPCC SERS. The error analysis methodology based on geostatistical simulation could model both uncertainties of the error assessment and error propagation problems in a probabilistic framework. Therefore, it is expected that the error analysis methodology applied in this paper will be effectively used for the probabilistic assessment of errors included in various thematic maps as well as the error assessment of waterline-based DEMs in tidal flats.

• Geophysics and Geophysical Exploration
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• v.19 no.4
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• pp.187-199
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• 2016

In order to delineate the soft ground distribution, an integrated geostatistical analysis was performed using the MASW (Multichannel Analysis of Surface Wave) which has the information of overall region and CPTu (Piezo Cone Penetration Test) which provides the direct information of the measuring point of the ground. MASW results were known to have close relationship with the ground stiffness. This correlation was confirmed through the comparison of MASW data obtained from two survey lines to the laboratory test with extracted soil samples. 3D physical property distribution in the study area was acquired by geostatistical integrated analysis with the data of tip resistance ($q_c$) and pore pressure (u) from the CPTu obtained at 6 points within the study area. The integrated analysis was conducted by applying the COSGSIM (Sequential Gaussian Co-Simulation) technology which can carry out the simulation in accordance with the spatial correlation between the MASW results and both tip resistance and pore pressure. Besides the locations of CPTu, borehole investigations were also conducted at two different positions. As a result, the N value of SPT and borehole log could be secured, so these data were used for the analysis of the geotechnical engineering accuracy of the integrated analysis result. For the verification of reliability of the 3D distribution of tip resistance and pore pressure secured through integrated analysis, the geotechnical information gained from the two drilling areas was compared, and the result showed extremely high correlation.