• 한국지구물리탐사학회:학술대회논문집
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• 2004.06a
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• pp.284-288
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• 2004

• Journal of the Korean Geophysical Society
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• v.4 no.2
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• pp.133-142
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• 2001

The rapid expansion of Internet foundation made it possible to handle various data and information with ease and swift. The HTML is widely used in this circumstace to transfer information via Internet. Howeverm the HTML has limit to describe all aspect of data and information, and the XML is proposed for the replacement. The XML is Internet language that supplies more diverse methods to classify and describe the contents. In this stuydy, some advantages to process geophysical data by XML is descussed and teleseismic data recorded in KMA were gransformed to the XML information.

• Geophysics and Geophysical Exploration
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• v.12 no.4
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• pp.309-315
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• 2009

Integrated interpretation of multi-geophysical data has been continuously used in terms that it has provided more confident information than the result from single-geophysical data. Especially, geostatistical integration has its own superiority that it is possible to deal with spatial characteristics as well as physical properties of survey data and the process of integration is clear. This paper further extends the previous work of geostatistical inversion for integrated interpretation. In this paper, we propose a new way of dealing with the case that the multi-geophysical data do not share the measurement range. According to the geostatistical kriging, the closer between the measurement points, the smaller kriging variance we get, and vice versa. We used this spatial properties as a weighting value to the process of geostatistical inversion for the geophysical data integration. An objective way to integrate different kinds of geophysical data measured at different ranges is provided with this algorithm.

• Journal of the Korean Geophysical Society
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• v.6 no.1
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• pp.47-47
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• 2003

• Journal of the Korean Geophysical Society
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• v.3 no.3
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• pp.161-174
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• 2000

Bayesian inversion is a stable approach to infer the subsurface structure with the limited data from geophysical explorations. In geophysical inverse process, due to the finite and discrete characteristics of field data and modeling process, some uncertainties are inherent and therefore probabilistic approach to the geophysical inversion is required. Bayesian framework provides theoretical base for the confidency and uncertainty analysis for the inference. However, most of the Bayesian inversion require the integration process of high dimension, so massive calculations like a Monte Carlo integration is demanded to solve it. This method, though, seemed suitable to apply to the geophysical problems which have the characteristics of highly non-linearity, we are faced to meet the promptness and convenience in field process. In this study, by the Gaussian approximation for the observed data and a priori information, fast Bayesian inversion scheme is developed and applied to the model problem with electric well logging and dipole-dipole resistivity data. Each covariance matrices are induced by geostatistical method and optimization technique resulted in maximum a posteriori information. Especially a priori information is evaluated by the cross-validation technique. And the uncertainty analysis was performed to interpret the resistivity structure by simulation of a posteriori covariance matrix.

• Geophysics and Geophysical Exploration
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• v.3 no.1
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• pp.25-32
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• 2000

The geophysical data management system was developed to meet both the increasing demands of geophysical data in the practical application of civil engineering, underground water survey, and environmental problems and needs for digital archive and quality control of geophysical data. The system for a data manager is developed under Client/Server (C/S) environment. This manager system is characterized by a relational geophysical database system using MS SQL-server, standardization of geophysical data format, the development of C/S interface program for Windows environment, and the development of transfer program module for the searched data. The system developed for a general user under the internet environment is characterized by Web service (URL:http//geophy.kigam.re.kr) and the development of plug-in module to visualize geophysical image data.

• 한국지구물리탐사학회:학술대회논문집
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• 2004.06a
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• pp.123-127
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• 2004

• 한국지구물리탐사학회:학술대회논문집
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• 2005.05a
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• pp.245-250
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• 2005

• 한국지구물리탐사학회:학술대회논문집
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• 2008.10a
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• pp.63-67
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• 2008

Geostatistical inverse approach using geophysical data was applied to indirectly make the RMR classification at points apart from boreholes. The geostatistical appoach was usually used to find optimized estimation which supports two or more different physical properties at unsampled points. However, in this study, an approach to solve inverse problem was proposed. The primary variable, RMR values obtained at known boreholes, is geostatistically simulated with many realization at pre-defined grid point according to the variogram model. The simulated values are sequentially compared with the physical property resulted from geophysical survey at an arbitrary grid point, and the most similar one is chosen. This process means that the spatial distribution of primary variable, RMR, is conformed well to the original pattern of the borehole observation, and ensure to fit the geophysical survey result to reflect the correlation between different physical properties.

• Geophysics and Geophysical Exploration
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• v.7 no.2
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• pp.121-129
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• 2004

The geostatistical approach was applied to integrate MT (Magneto-telluric) resistivity data and borehole information for the spatial RMR (Rock Mass Rating) evaluation. Generally, resistivity of the subsurface is believed to be positively related to the RMR, thus the resistivity and borehole RMR information was combined in a geostatistical approach. To relate the two different sets of data, we take the MT resistivity data as secondary information and estimate the RMR mean values at unsampled points by identification of the resistivity to the borehole data. Two types of approach are performed for the estimation of RMR mean values. Then the residuals of the RMR values around the borehole sites are geostatistically modeled to infer the spatial structure of difference between real RMR values and estimated mean values. Finally, this geostatistical estimation is added to the previous means. The result applied to a real situation shows prominent improvements to reflect the subsurface structure and spatial resolution of RMR information.