Hi All,

 

Thanks for the many replies thus far. Just to make it clear: the data has not been normalised. The samples (from drill core) have simply been composited to equal support lengths. Sorry about the confusion between gamma and rho, but I think you know what I mean – the left hand axis of the variogram graph.

 

Theoretically, nugget + sill = sample variance, except in the absence of trend or drift. Mario seems to have seen exactly what the problem is. There is indeed zonal anisotrophy in one particular direction, for the variogram under question.

 

I have considered both Mario’s suggestions:

 

1. Adding the last structures variance component, keeping the range the same. In fact, I wanted to add the component to the nugget effect (a sort of conservative approach by allocating more of a random component to grade variability, if that makes sense), but I was advised against this. It was suggested I try ‘scaling’, which is where I am coming unstuck.

 

2. Manipulating the 2nd structure, but then ending up with a massive range, which as Mario has correctly predicted, raises eyebrows. I also run the risk of negative weights because of the very large search area. The intermediate direction is rather poor too, so this is perhaps not an option.

 

I hope that explains the problem better.

 

Regards,

Colin

 


From: Mario Rossi [mailto:[EMAIL PROTECTED]
Sent: 07 February 2006 10:51
To: Colin Badenhorst
Subject: Re: [ai-geostats] Sill not reaching 1

 

Colin,

what may be happening is that you have a strong zonal anisotropy, I'm assuming you're talking about a specific direction.

You probably know that the zonal can be either modeled as:

a) an additional nested structure in the specific direction where it occurs, just add the variance component, same range; or

b) convert the zonal into a geometric anisotropy, taking the last nested structure  asymptotically until it reaches 1.0 at a very long distance in that direction. Easier done than explained, it may raise eyebrows in people that don't understand or have a geostats background when you try to explain 2-3 5km ranges in a 500m long deposit, for example... Also, need to be careful with intermediate directions, and make sure they fit well.

 

Hope this helps,

 

Cheers,

 

Mario

Colin Badenhorst <[EMAIL PROTECTED]> wrote:

Hi All,

 

I am performing estimation for Ni as a penalty element to our main ore, using nested spherical correlogram models. One of these shows abundant and robust experimental data reaching a plateau at gamma = 0.90, and is thus modelled in such a way that the sill of this spherical model is at 0.9, instead of at 1.00.

 

I’m not sure of this, but is there a strict requirement that the sill differentials and nugget need to sum to 1.00 for estimation purposes. I use Vulcan (effectively GSLIB) for estimation. If it is a requirement that they add to 1.00, it has been suggested that I could scale the nugget and sill to a value of 1, but I’m not sure exactly how to do this.

 

Does anyone have any comments and/or suggestions?

 

Regards,

Colin



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