Question 1,

You may be interested in some of these points on lognormal

kriging, as I have been involved with lognormal kriging of datasets,

but not in use for unconditional simulation.

- Lognormal kriging can be very sensitive. If you plot experimental

variograms of your lognormal dataset, note that if your variograms

are well formed you are in luck, however if your variograms are very

erratic, note that your grades will be in error in proportion of the error

in your estimation of the sill.

- Yes prior to lognormal kriging you log transform the data so it has

zero skewness and is normally distributed. Note that if your data

belongs to a "three" parameter lognormal population you must also

make an estimte of the third parameter alpha, and add this to your

values before taking their logarithms.

If you plot a log probability plot of your data and it is a straight line

then you have a two parameter lognormal distribution and you can

take logarithms of the data to acheive zero skewness.

If the line drops of towards the origin you may have a three parameter

lognormal population with which you can estimate alpha from the

graph or alternatively iterativley trial different values to your population

until it has a skewness of zero i.e.

z=ln(x+alpha)

where z is the transformed distribution which has skewness=0;

- Also note that the anti-logarithm of a number is not equal to the

logarithm of a number, so after your modelling, you cannot back

transform your data, by simply taking the antilogarithm of the values.

You will have to check a geostatistical text to see the procedure for

back transformation of data.

Question 2,

I have just read Margaret Armstrongs "Basic Linear Geostatistics" and

in the chapter on Structural Analysis provides three case studies, and in

all case studies uses the same nugget effect for all directions, even though

in two of the case studies there is variation of the nugget effect in different

directions as in your case.

I would be inclined to use the omidirectional nugget effect in your case, the

values are reasonably similar as in the case studies. It is possible the nugget

effect varies in the different directions due to the different spacing of the data

in the different directions, and the data is samples also, so may not perfectly

follow the real underlying values of the actual continuous data.

Regards Digby Millikan B.Eng

Geolite Mining Systems

U4/16 First Ave.,

Payneham South SA 5070

Australia.

Ph: +61 8 84312974

digbym@...

http://www.users.on.net/digbym

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