Development of a selection system of the optimal sampling locations for a soil pollution survey.

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A new method for optimal strategy of sampling locations for soil pollution was developed using geostatistics. The evaluation function was created as a estimate, of the differences between geostatistically. estimated fields and fictitious real fields created by the unconditional simulation, which was a geostatistical Monte Carlo method. The difference was evaluated by sum of squares of the difference of concentration at each location in a whole field or a square of the difference of the sum of a pollution material in a whole field. The method could also treat with uncertain information by selecting fields which could meet the given information from the fictitious real fields created by the unconditional simulation. Improvement of genetic algorithms and development of a new method named "stepwise movement to the best point (SMBP)" which could always reach a local solution were executed. Their hybrid method was shown to be effective for searching the optimal solution. Characteristics 'and problems in the parameter estimation by most likelihood method was also studied. The results showed that the correlation scale estimated by the most likelihood method was apt of be smaller than the real one and that uncertainty at unknown points could not be necessarily estimated properly using the estimated parameters. The examples of soil pollution released by Japanese government showed that correlation scales estimated using the examples had log-normal distribution. Using these data as the experiential prior information, parameter estimation was thought to be improved.