[GRASS-user] Searching Docs about 3D geological modelisation

[Dylan Beaudette]

Two more ideas:

1. conditional simulation, based on a 3D variogram model
2. transition probability-based interpolation of categories

Check out gstat for the conditional simulation, and TPROGS for the
transition probability. If anything is interested, I have done some
programming to connect GRASS and TPROGS.

Cheers!

Dylan

On Fri, Jan 8, 2010 at 1:24 PM, Benjamin Ducke
<benjamin.ducke at oxfordarch.co.uk> wrote:
> Woohoo, this forum is always a treasure trove
> of good advice. I had not idea SGemS existed!
> The Voronoi idea is also good, I am just not sure
> that the 3D Voronoi diagram is quite what one
> would instinctively think it is.
>
> http://en.wikipedia.org/wiki/Voronoi_diagram
>
> says: "In general a cross section of a 3D Voronoi
> tessellation is not a 2D Voronoi tessellation itself."
>
> Need to look into that.
>
> I don't have much practical experience
> with Bayes models, so can't really comment on
> that.
>
> Cheers,
>
> Ben
>
>
> Christian Kaiser wrote:
>> It seems to me that this is a 3D interpolation problem with categorical variables.
>>
>> Maybe the Bayesian Maximum Entropy approach could help. There are some interesting publications around also for geology and soil sciences, and they can deal with categorical data as well. Look for example here: http://www.enge.ucl.ac.be/staff/curr/Bogaert/biblioBME/BMEbibsubject.html#Soil%20Science
>>
>> Or maybe you can have a look at SGeMS (http://sgems.sourceforge.net), a tool for 3D geostatistics.
>>
>> None of them is available through GRASS, but the algorithms are freely available (I think open-source, but not verified).
>>
>> I am not a geologist, so please forgive if it is not adequate...
>>
>> Christian Kaiser
>>
>>
>>
>>
>>
>> On 8 janv. 2010, at 11:04, Benjamin Ducke wrote:
>>
>>> Rich Shepard wrote:
>>>>> material. There is no interpolation algorithm in GRASS currently which
>>>>> can
>>>>> handle that sort of data well.
>>>>  So what is needed is a political algorithm. :-)
>>> That's actually right: given the presence of n different
>>> layer types in the vicinity of an empty voxel, the algorithm
>>> would need to decide by some sort of "majority vote"
>>> which type to assign to that voxel.
>>>
>>>>  Kidding aside, I suspect that a fuzzy interpolation algorithm would solve
>>>> the problem.
>>> How? You could make the interpolated value depend on a
>>> fuzzy set member function, I suppose, but the situation
>>> here is actually so well defined that I think a probabilistic
>>> approach would be preferable. Since each voxel can only
>>> store one value, a second output map could store the
>>> classification probability. That may be very useful
>>> for visualization (you could show voxels with little
>>> probability hazier).
>>>
>>> Ben
>>>
>>>> Rich
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>>>>
>>>
>>> --
>>> Benjamin Ducke
>>> Geospatial Consultant
>>>
>>> Oxford Archaeology Digital
>>> Janus House
>>> Osney Mead
>>> OX2 0ES
>>> Oxford, U.K.
>>>
>>> Tel: +44 (0)1865 263 800 (switchboard)
>>> Tel: +44 (0)1865 980 758 (direct)
>>> Fax :+44 (0)1865 793 496
>>> benjamin.ducke at oadigital.net
>>> http://oadigital.net
>>>
>>>
>>>
>>>
>>>
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>>
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>>
>
>
> --
> Benjamin Ducke
> Geospatial Consultant
>
> Oxford Archaeology Digital
> Janus House
> Osney Mead
> OX2 0ES
> Oxford, U.K.
>
> Tel: +44 (0)1865 263 800 (switchboard)
> Tel: +44 (0)1865 980 758 (direct)
> Fax :+44 (0)1865 793 496
> benjamin.ducke at oadigital.net
> http://oadigital.net
>
>
>
>
>
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>
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