AI-GEOSTATS: SAGE2001 and GSLIB
Hi all, I'd like to ask who has experience with both SAGE2001 and GSLIB? SAGE2001 is a software application developed by Isaaks, the author of a book titled "An Intoductioin to Applied Geostatistics". It can detect the direction of anisotropy by brutal force. Attached is a screen shot of the model fitted by this application, which consists of 2 spherical structures, each having its own independent anisotropy defined. As you know, GSLIB's kriging programs make use of 3 ranges and 3 angles to define the anisotropy: 1. *aa_hmax:* the maximum horizontal range(Is this the major range?) 2. *aa_hmin:* the minimum horizontal range (Is this the median one?) 3. *aa_vert:* the vertical range (Is this the minor one and should it be shorter than the above two? GSLIB does not check the order of these 3 numbers.) 4. *ang1, ang2, ang3* Taking the first structure in the attached image as an example, could any of you having expertise in these 2 applications give me some guidance as to how the 3 ranges and 3 angles calculated by Sage are mapped to GSLIB's corresponding input parameters? Some software packages use major, median, and minor to denote the 3 ranges and requires that major > median > minor, different from GSLIB's definition for these 3 ranges. Many thanks in advance for your help! Regards, Yang
AI-GEOSTATS: Re: Sign of the Lagrange Multiplier Used in Back-transform
Hi Isobel, I used correlogram as the estimator, so the Lagrange multiplier should be added to rectify the variance. Many thanks for your help! Regards, Yang On Mon, Dec 21, 2009 at 3:31 PM, Isobel Clark wrote: > Yang > > Yes the lagrangian multipier is subtracted, assuming you used the > semi-variogram in your kriging equations. If you use the covariance, it is > added. > > The extra terms in the back transform are to correct for the difference > between the variance of the true values and the variance of the estimators. > If you are estimating at points, the estimator is a weighted average which > will have a smaller variance than single point values. Back transforming > values with a smaller variance will bias the estimates downwards. > > If you want unbiassed estimated values, you have to follow the formula. > > Hope this helps > Isobel > > http://drisobelclark.kriging.com > > --- On Mon, 21/12/09, yang yu wrote: > > > From: yang yu > > Subject: AI-GEOSTATS: Sign of the Lagrange Multiplier Used in > Back-transform > > To: ai-geostats@jrc.it > > Date: Monday, 21 December, 2009, 21:02 > > Hello all, > > > > I'm trying to apply the lognormal kriging method > > to a highly negatively skewed dataset (data were reflected > > first). The back_transform formula given in the reference > > book takes the following form: > > > > Z(x) = EXP[ EstimatedValue + KrigingVariance/s - > > LagrangeMultiplier] > > > > > > in which the Lagrange multiplier is subtracted from the the > > first 2 items. Is this formula assuming that the Lagrange > > multiplier value calculated for each block/cell is POSITIVE? > > All of the Lagrange values I got for my dataset are > > NEGATIVE. In this case, should the negative Lagrange values > > be ADDED to the first 2 items? > > > > > > Many thanks for any guidance and happy hollidays > > > > Regards, > > Yang > > >
AI-GEOSTATS: Sign of the Lagrange Multiplier Used in Back-transform
Hello all, I'm trying to apply the lognormal kriging method to a highly negatively skewed dataset (data were reflected first). The back_transform formula given in the reference book takes the following form: Z(x) = EXP[ EstimatedValue + KrigingVariance/s - *LagrangeMultiplier*] in which the Lagrange multiplier is subtracted from the the first 2 items. Is this formula assuming that the Lagrange multiplier value calculated for each block/cell is POSITIVE? All of the Lagrange values I got for my dataset are NEGATIVE. In this case, should the negative Lagrange values be ADDED to the first 2 items? Many thanks for any guidance and happy hollidays Regards, Yang
