AI-GEOSTATS: Kriging versus inv. Dist. Weighting
- Dear all,
This is my first try at geostat mailing list, and maybe my question will not be very "professional".
I work with data set of porosity in one oil reservoir. Interpolations were done with three interpolation methods: Inverse distance weighting, Kriging (ordinary) and Cokriging (collocated). I done spatial analysis with semivariogram modelling for (co)Kriging.
After all, I calculated true error for every included point as difference between real value and estimated value at the same place. I was confused when I saw that Kriging error was higher of Inverse Distance Weighting error! The lowest errors were gained by Cokriging (with the same semivariogram modell as used in Kriging).
What could be reason for that? Maybe 14 points is too low set for proper modelling of directional semivariogram analysis (directions=0 and 90 degrees). I tested several lag distances and distance with the highest range was chosen. If chosen distance is too low interpolation map contains mostly areas of "bull-eyes". Also, input points are moderately clustered.
Thank you and best regards,
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