• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2001.09a
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• pp.92-96
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• 2001

Although the problem of seawater intrusion at the coastal aquifer was recognized before over one hundred years at the coastal aquifer, much groundwater keep on being salinitized by several reasons such as groundwater exhaustion, coastalline change, and human activities. The horizontal and vertical electrical soundings and geostatistical methods were used to define the local characteristics of saltwater intrusion and to estimate the saltwater interface in the southeastern area of the Pusan City. The 24 points of the Schlumberger vertical electrical soundings(VES) to loom depth and the 2 lines of dipole-dipole horizontal soundings are peformed. The resistivity data have lognormal distributions. The horizontal extents of saline water intrusion were estimated from the inversion of horizontal prospecting data. Lognormal ordinary kriging is used in A-A' resistivity profiles on May and July because the data have stationary models in semivariograms. Lognormal IRF-k kriging is used for the isopleth maps using vertical resistivity data. The 10 ohm-m resistivity line on the isopleth maps of 21m, 30m, 50m, and 70m depth using resisitivity data measured in July is sifted to the east, cpomparing that of the isopleth maps measured in May. The kriged vertical and horizontal resistivity isopleth maps suggested that the geostatistical methods can be used to define the variation of earth resistivity distribution at the saltwater interface.

• Korean Journal of Remote Sensing
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• v.7 no.1
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• pp.45-67
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• 1991

An overview is given on stochastic techniques with which remotely sensed data may be used together with ground measurements for purposes of state estimation and prediction. They can explicitly account for spatiotemporal differences in measurement characteristics between ground measurements and remotely sensed data, and are suitable for highly variant space or space-time processes, such as atmosperic processes, which may be viewed as (containing) a random process. For state estimation of static ststems, optimal linear estimation is described. As alternatives, various co-kriging estimation techniques are also described, including simple, ordinary, universal, lognormal, disjunctive, indicator, and Bayesian extersion to simple and lognormal. For illustrative purposes, very simple examples of optimal linear estimation and simple co-kriging are given. For state estimation and prediction of dynamic system, distributed-parameter kalman filter is described. Issues concerning actual implemention are given, and with application potential are described.