Byung-Doo Kwon

Improved frequency‐domain elastic wave modeling using weighted‐averaging difference operators

We develop a new finite-difference scheme that reduces the number of grid points per wavelength required in frequency-domain elastic modeling. Our approach computes weighted averages of the spatial second-order derivative and the mass acceleration terms using a 25-point computational stencil. By determining the weighting coefficients to minimize numerical dispersion and numerical anisotropy, we reduce the number of grid points to 3.3 per shear wavelength, with a resulting error in velocities smaller than 1%. Our choice of grid points reduces the computer memory needed to store the complex impedance matrix to 4% of that for a conventional second-order scheme and to 54% of that for a combined second-order scheme. The 25-point weighted averaging scheme of this paper makes it possible to accurately simulate realistic models. Numerical examples show that this technique can achieve the same accurate solutions with fewer grid points than those from previous frequency-domain second-order schemes. Our technique can be extended directly to 3-D elastic modeling; the computational efficiency will be even greater than that realized for 2-D models.

Geostatistical approach to bayesian inversion of geophysical data: Markov chain Monte Carlo method

This paper presents a practical and objective procedure for a Bayesian inversion of geophysical data. We have applied geostatistical techniques such as kriging and simulation algorithms to acquire a prior model information. Then the Markov chain Monte Carlo (MCMC) method is adopted to infer the characteristics of the marginal distributions of model parameters. Geostatistics which is based upon a variogram model provides a means to analyze and interpret the spatially distributed data. For Bayesian inversion of dipole-dipole resistivity data, we have used the indicator kriging and simulation techniques to generate cumulative density functions from Schlumberger and well logging data for obtaining a prior information by cokriging and simulations from covariogram models. Indicator approaches make it possible to incorporate non-parametric information into the probabilistic density function. We have also adopted the Markov chain Monte Carlo approach, based on Gibbs sampling, to examine the characteristics of a posterior probability density function and marginal distributions of each parameter. The MCMC technique provides a robust result from which information given by the indicator method, that is fundamentally non-parametric, is fully extracted. We have used the a prior information proposed by the geostatistical method as the full conditional distribution for Gibbs sampling. And to implement Gibbs sampler, we have applied the modified Simulated Annealing (SA) algorithm which effectively searched for global model space. This scheme provides a more effective and robust global sampling algorithm as compared to the previous study.

Quantitative indices based on 3D discrete wavelet transform for urban complexity estimation using remotely sensed imagery

Urbanization in most developing countries is gradually increasing. Quantitative estimation of urbanization to a standardized level is crucial for analysing the relation between urbanization and other factors, since urban growth affects diverse and complex natural and socio-economic phenomena. Spatially and spectrally, remotely sensed imageries covering urban areas display different characteristics from those of rural or mountainous areas. A three-dimensional discrete wavelet transform (3D DWT) scheme is regarded as useful for analysing both spatial and spectral information. In addition, the energy parameter of wavelet coefficient by 3D DWT indicates channels regarding dominant spatial and spectral frequency, extracted from the original image sets. Based on these spatial and spectral features and the energy parameter of 3D wavelet coefficients, three kinds of urban complexity index are proposed. Of the three indices, the third index is the most useful to quantitatively interpret urban complexity. These indices can be used for a basis to analyse the relationship between feature groups within urban environments.

Geostatistical integration of spectral and spatial information for land-cover mapping using remote sensing data

A geostatistical contextual classifier for land-cover mapping using remote sensing data is presented. To integrate spatial information with spectral information derived from remote sensing data, a geostatistical indicator approach is adopted to determine the probability of a certain land-cover class occuring at an unsampled location given that any other land-cover classes occur at neighboring locations. The geostatistical indicator algorithm applied here is simple indicator kriging with local means. This approach can directly integrate both spatial information of ground data (hard data) and spectral information of remote sensing data (soft data) within an indicator kriging framework. This algorithm is applied to the classification of multi-sensor remote sensing data for land-cover mapping. This classification result is compared with a result obtained from a conventional spectral information based classification method.

Tectonic Structure Modeling around the Ulleung Basin and Dokdo Using Potential Data

The East Sea including the area of this study is identified as a typical back-arc sea located in the backside of the Circum-Pacific volcanic and earthquake belt. Previous studies reported that the East Sea has begun to open by tensile force and formed its current shape. In this study, we investigate the regional tectonic structure of the East Sea using ship-borne gravity, magnetic, and satellite gravity data. The result of three-dimensional depth inversion shows that Moho depth of the study area is approximately 13-25km and inversely proportional to the thickness of the crust. In addition, as approaching to the center of the Ulleung Basin (UB), the thickness of the crust of the UB becomes thinner due to the extension caused by tensile force which had opened the East Sea.

Multi-sensor data fusion for supervised land-cover classification using Bayesian and geostatistical techniques

We propose a geostatistical approach incorporated to the Bayesian data fusion technique for supervised classification of multi-sensor remote sensing data. The classification based only on the traditional spectral approach cannot preserve the accurate spatial information and can result in unrealistic classification results. To obtain accurate spatial/contextual information, the indicator kriging that allows one to estimate the probability of occurrence of certain classes on the basis of surrounding pixel information is incorporated into the Bayesian framework. This new approach has its merit incorporating both the spectral information and spatial information and improves the confidence level in the final data fusion task. To illustrate the proposed scheme, supervised classification of multi-sensor test remote sensing data was carried out. Analysis of the results indicates that the proposed method considerably improves the classification accuracy, compared to the methods based on the spectral information alone.

Application of template matching method to traffic feature detection using KOMPSAT EOC imagery

Currently, the application domains of the commercialized high resolution satellite imagery are in the middle of expansion. These images contain detailed information to investigate targets in the application level. In this paper, we try to demonstrate traffic analysis scheme based on template matching method algorithm. The simple matching algorithm implemented for this purpose is as follows: First, one user determine a type of vehicle or other traffic feature, as a template corresponding to a target feature. In this process, it is possible to choose multiple templates according to vehicle modes. Second, one can automatically detect and count the number of vehicles statistically similar to template given in the previous step through template matching method algorithm. While, if user already knows the geo-referenced location of a specific vehicle, traffic flow analysis in the static status is also possible in this step. Through the results from each counting vehicles, user can summarize statistics of traffic features at a certain specific time in high-resolution satellite imagery. As for the actual test and experiment, satellite imagery of KOMPSAT EOC was used, and KOMPSAT imagery consists of feature sets in an urban region showing complex types of spatial features. Results of template matching method can provide useful ones in detecting the types of vehicle in the remote traffic detection or monitoring system. In conclusion, this approach can be utilized to obtain some information about on-road traffics such as the location of vehicle, traffic volume, and other transportation parameters, related to transportation uses of remote sensing image.

Application of fuzzy set theory for spatial prediction of landslide hazard

This work presents a fuzzy logic scheme to integrate multi-source spatial data for landslide hazard mapping. Main focus of This work is on fuzzy information representation based on frequency ratio and non-parametric density estimation. Of particular interest is the representation of continuous data for preventing loss of information. The non-parametric density estimation method applied here is a Parzen window estimation that can directly use continuous data without any categorization procedure. A case study at Jangheung, Korea is presented to illustrate the proposed scheme.

GML-based representation architecture for digital geo-science GIS layers: a case study using Korea digital geologic map sets

Now most Internet map services are provided on the Web, but most of those approaches depend on images in graphic format. These types of image format require relatively large-sized files, and have some problems with data transport and storage on the Web. In addition, as the meaning of the data is not captured as parts of the data itself, data translation, when it happens, must then be accompanied by painstaking manual process to restore the meaning. To overcome these problems, GML (Geographic Markup Language) has developed as the Web standard format of geographic information. Although GML focus on describing the geographic information as the type of XML (eXtensible Markup Language), there are few cases applied to digital geologic maps till now. In this study, Internet mapping of a Korean digital geologic map was accomplished using GML as a case study. Digital geologic map sets consist of geologic boundary, geo-text, fault, cross section, lithology and other complex items to represent geo-based attributes. Each layer was described to both spatial and non-spatial information using GML profile proposed by OGC (open GIS Consortium, Inc.). Spatial and non-spatial information were described as geometry of GML and properties of geographic features, respectively. The geographic information described by GML was transformed into SVG (Scalable Vector Graphics), one of the XML standards for graphic format. Vector maps can be visualized via the Internet without proprietary software on the Web browsers. SVG-enabled browsers allow users to control their view with zoom and pan as they do so in GIS software. As the content and the representation of the geographic information were separated in the Internet mapping process of digital map, it is possible to produce multiple representation about the same geographic information and the dynamic customization on users need, and the spatial analysis on the Web. Conclusively, GML-base geologic map produced in this study can be transfer rapidly without loss of the meaning and degrading on the Web. So it can be effectively utilized to not only Internet map services but also LBS (Location Based Services) such as PDA or a mobile platform.

Analysis of a Structure of the Kunsan Basin in Yellow Sea Using Gravity and Magnetic Data

We studied a structure of the Kunsan basin in the Yellow Sea using ship-borne magnetic data and altimetry satellite-derived gravity data provided from the Scripps institution of oceanography in 2006. The gravity data was analyzed via power spectrum analysis and gravity inversion, and the magnetic data via analytic signal technique, pseudo-gravity transformation, and its inversion. The results showed that the depth of bedrock tended to increase as we approached the center of the South Central Sag in Kunsan basin and that the maximum and minimum of its depth were estimated to be about 6-8 km and 2 km, respectively. Inaddition, the observed high anomaly of gravity and magnetism was attributed to the intrusion of igneous rock of higher density than the surrounding basement rock in the center of South Central Sag, which was consistent with the interpretation of seismic data obtained in the same region.