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Hi Eric, Thank you for your useful reply. I'm confident this will help solving my problem. 🙂
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11-06-2011
10:04 PM
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Thank you, Eric, for your quick and helpful reply! Reading your answer on my second question I think I haven't made my second question sufficiently clear, though, so please give me a second chance: I have a network of monitoring wells, and I want to optimize it: I want to say on what locations the construction of extra monitoring wells would sufficienty lower the standard error. I thought that it was possible to calculate a 'wanted' semivariance value out of a 'wanted' maximum standard error. Then I could read that 'wanted' semivariance on the Y-axis of the semivariogram and would I get an X-value which forms then the 'wanted maximum distance between monitoring wells'. I thought it was possible to do so, and to get the semivariance out of the wanted standard error by taking the square of the latter. But if I'm wrong, is there a different way to do so? Thank you! 🙂
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11-04-2011
07:31 AM
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Hello all, I'm currently using the Geostatistical Analyst to perform some kriging interpolations, and interpret the results. However, I unfortunately stumbled up some questions, which I hope you have the answers for: First of all, when assigning weighting values to the points, I noticed that some points are assigned negative values. I understand how they come there (inverse of a matrix), but: what does that mean. How can points account negatively to an unknown point? Second question gets more clear in the example: I have a point file of monitoring wells measuring phreatic groundwater levels. I want to say something about the maximum distance between these wells in order to have a maximum uncertainty in an area (for instance a village, where houses can be damaged due to high or low water tables). So I set a level: I don't want my standard error to be larger than 20 centimeters, a value that does occur on the prediction error map. I thought that I should take the square of 20 cm, 4cm^2 as semivariance input to the model and that the x-value was then my maximum distance of the wells (which could be easily turned into a preferred well density). However, in the table of the exported semivariogram, this value of 0.04 doesn't exist, in fact, only the non-squared values of the map are in that table. So I wonder: how can it be that the values of the semivariance - which is given in [m^2] - are the same as the values of the prediction error map - given in . I hope you can help me with it. Best regards, and thank you in advance, Erik (Dirk's trainee)
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11-04-2011
05:09 AM
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