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I am having trouble interpolating groundwater nitrate values using Kriging. Once I tell the tool to optimize the fit it produces very sharp edges in the spatial domain that I have a hard time justifying using physical concepts. I was wondering if anybody else has had this issue and how could one deal with this. I would like to create a more smooth interpolated map if possible. Attached is the figure I have with the black points indicating the sample points.
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10242013
06:14 AM

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Thank you Eric6346..... I will try this  though I have to read up on cokriging to understand the assumptions that I have to make regarding a crosscovariance curve. Any hints on where to find some quick reading on this topic ? Thanks again. My recommendation would be to not attempt to merge the two datasets. Instead, perform cokriging. Cokriging will use information from both datasets to make predictions, but it won't assume that the two datasets have the same statistical properties. Even if the two datasets do have identical statistical properties (implying you can safely merge them), you won't lose much by performing cokriging compared to merging the datasets and performing univariate kriging. The only disadvantage is that you will have to estimate two semivariograms and one crosscovariance curve rather than just one semivariogram.
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03132013
03:43 PM

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I have two datasets that have the same information (point values of groundwater level). However one is of 2007 and the other of 2001. I would like to know if there is a method, tool or way to prove statistically that these datasets can be joined to be one or a way that I can prove that they can't be joined. More specifically, I have 46 points in one data set that intorpolating gives me one map and another with 60 points that give me another map. The points are all in different locations. Ideally I would like to join the 106 points into one data set in order to create a more accurate interpolation. But to do that I would have to prove statistically that these points are from the same population. In common statistical analysis there are several tests that could be done. However with geostatistics I am not sure how to proceed. Any help in the form of advice, pointing to a previous post, a guide or a solution would be greatly appreciated. Thank you
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03122013
04:21 AM

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286

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I have two data sets that have the same information (point values of groundwater level). However one is of 2007 and the other of 2001. I would like to know if there is a method, tool or way to prove statistically that this datasets can be joined to be one or at least a way that they can't be joined. More specifically. I have 46 points in one data set that intorpolating gives me one map and another with 60 points that give me another map. The points are all in different locations. Ideally I would like to join the 106 points into one data set in order to create a more accurate interpolation. But to do that I would have to prove statistically that this points are from the same population. In common statistical analysis there are several tests that could be done. However with geostatistics I am not sure how to proceed. Any help in the form of advice, pointing to a previous post, a guide or a solution would be greatly appreciated. Thank you
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03112013
01:50 PM

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