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

Sat Jan 9 09:50:22 EST 2010

it seems to me that the TPROGS word only refers tp a method and not a 
software, here is slide I found about if by Gary Weissmann (from 
google's cache as the pdf's link is dead) :

http://209.85.229.132/search?q=cache:5JU5t4eQ4HYJ:www.isgs.illinois.edu/research/3DWorkshop/2005/pdf-files/Weissmann-GSA-2005_ppt.pdf+TPROGS&cd=24&hl=fr&ct=clnk&gl=fr&client=firefox-a

 From forum's post he seems to have a software package available for 
transition probability geostatistics which he then import into GSM (the 
software linked in the previous mails)

Le 09/01/2010 11:51, Benjamin Ducke a écrit :
> Cheers for these. They are certainly all highly interesting.
> Do you have an actual link for the T-PROGS software itself?
> All I can seem to come up with are interfaces from other
> software and publications mentioning it.
>
> I would certainly be interested in taking a look at your
> GRASS interface. Is T-PROGS open source?
>
> My gut feeling is that the T-PROGS approach would give better
> results than 3D kriging, as it seems better able to to
> follow 3D shape trends:
>
> http://chl.erdc.usace.army.mil/chl.aspx?p=s&a=ARTICLES;37&g=50
>
> ... but that certainly would need testing.
>
> Having said that, I also like this approach for a more
> heuristic model:
>
> http://chl.erdc.usace.army.mil/chl.aspx?p=s&a=ARTICLES;41&g=50
>
> It's very simple and could easily be implemented directly
> in GRASS GIS. In fact, I coded something very similar to this
> for archaeological stratigraphy reconstruction a while back.
>
> Cheers,
>
> Ben
>
>
> ----- Original Message -----
> From: "Dylan Beaudette"<dylan.beaudette at gmail.com>
> To: "Benjamin Ducke"<benjamin.ducke at oxfordarch.co.uk>
> Cc: "GRASS user list"<grass-user at lists.osgeo.org>
> Sent: Saturday, January 9, 2010 4:30:40 AM GMT +01:00 Amsterdam / Berlin / Bern / Rome / Stockholm / Vienna
> Subject: Re: [GRASS-user] Searching Docs about 3D geological modelisation
>
> 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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>>
>>
>> --
>> 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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