Abstract
In this paper, a modified version of continuous simulated annealing is proposed as a tool for optimizing spatial sampling schemes at the point level. Spatial information on the sampling area, stored in a GIS, can be dealt with as constraints to the optimization process. Maps and earlier measurements can be handled as pre-information. The algorithm can distinguish different quantitative optimization criteria. In this paper, two criteria will be presented. The first ensures optimal estimation of the variogram, by reproducing a pre-specified point-pair distribution over distance- and direction classes. Compared to a previous study, the algorithm produced dramatic improvements. The second criterion aims at even spreading of the sampling points over the area. Here, the algorithm always did better than a triangular grid, especially in areas with much sampling constraints, where improvements could be up to 30%. Combination of the criteria in a phased sampling procedure is possible.
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© 1997 Springer Science+Business Media Dordrecht
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Van Groenigen, J.W. (1997). Spatial Simulated Annealing for Optimizing Sampling. In: Soares, A., Gómez-Hernandez, J., Froidevaux, R. (eds) geoENV I — Geostatistics for Environmental Applications. Quantitative Geology and Geostatistics, vol 9. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-1675-8_29
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DOI: https://doi.org/10.1007/978-94-017-1675-8_29
Publisher Name: Springer, Dordrecht
Print ISBN: 978-90-481-4861-5
Online ISBN: 978-94-017-1675-8
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