Modelamiento Implicito - VULCAN

8/10/2019 Modelamiento Implicito - VULCAN

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Mapteks Vulcan Implicit Modeller, the new kid

on the block

Ron Reid

May 30, 2014

In case you have happened to miss the saturation marketing, Maptek has recently released its

assault on Leapfrog in the form of its Implicit Modelling Module released with Vulcan 9.

The Vulcan IM module is not a true RBF implicit modelling method, rather it utilises Ordinary

kriging to build a blockmodel and emulate implicit modelling, much like GeoVias dynamic

shells (which utilises Inverse Distance estimation) and CAE Studios Implicit Shells (Inverse

distance or Ordinary Kriging). In a strange twist Maptek have solved the memory management

issue that plagues RBF functions and actually have an RBF implicit modelling algorithm. They

have chosen to use this in their Eureka software, a tool designed for regional exploration and

data visualisation rather than their more widely known (and used) Vulcan mine planningsoftware. I believe this decision has roots in the 32 bit / 64 bit issue (Maptek can correct me if I

am wrong), Eureka is a born and bred 64 bit program whereas Vulcan is historically a 32 bit

program and 32 bit systems are unable to handle the memory requirements. Whilst I have not

used Eureka Beta myself I got to see a demonstration of the Beta at the Vulcan Users

Conference last year (thank you Maptek for the invitation) and it certainly looks more than

capable. It is able to create surfaces and generate non-intersecting surfaces for seam / vein

modelling etc) and solids of grade and object data. Searches can be controlled using multiple

ellipses, polylines (with normals to control inside/outside) and input points can be edited to add

new information to control the interpolated boundary. As well as drillhole data any data that

contains points (points, lines and triangulations) can be modelled. The output appears

reasonable as indicated in Figure 1. All in all from the demonstration it appears to be quite achallenger even in its early formbut only available to those select few that own and use

Eureka.


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Figure 1. Implicit models of a drillhole dataset using Eureka.

Now to the program under discussion, as mentioned Vulcan 9 has introduced a new module

they call Implicit modelling and it comes at no cost for anyone owning the standard geology

tools modules. The menu shown below contains the various options available;

Yep, not many options but what does the Implicit Modelling Editor do?

The editor fires up a standard Vulcan form with a series of options on the left. The first option is

the Open Specifications page which allows you to select a parameter filewhen selected it will

populate all the fields with saved parameters, if you type in a new parameter name the options

you enter will be saved in this parameter file.


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You have the option of selecting a categorical model (such as lithology) or a grade model

which is either categorical (as in indicators) or continuous. You can model from a drillhole

database, or from an Isis composite file. If you select from the DH database you are offered the

ability to use the data as is or generate a composite filegiven the estimate is an ordinary

kriged estimate, compositing the data to a regular sample support is recommended. If you do

select an already formed composite database you are not required to select a composite

length. The database is used to create a distance Map file a 3D point file that contains the

distances away from the 0 point for each category/grade cut-off which is then used to

estimate each variable into the blockmodel. Both the Distance_Map file and the blockmodel arewritten to the project folder. You can either set up a new blockmodel by defining a name and

setting up blocksize, origin and extentsor you can select a pre-existing model. Uniquely the

distances are negative inside and positive outsidethe reverse of that seen in Leapfrog or

Micromine.


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Figure 2. Various options for setting up the estimate.

Like Micromine, Vulcans method is technically a local method as it uses a local search as partof the OK estimator and as a result the search parameters need to be set up prior to running

the estimate. You can limit the output (as in the categorical shellsnot grade shells) to a

topographic surface and amend how you want to handle the data, select search parameters

and setup a variogram for each category. The variogram can be advanced as in a standard

variogram you have modelled, or you can use a slider that generates a simple variogram that

varies between a continuous shape to a smooth shapenot a lot of information on what either

option means but it essentially creates a single structure spherical model and adjusts the


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nugget value. Setting it at very continuous sets the nugget to 5%, very smooth sets the nugget

to 95%, ranges are based on the search ellipse you have entered. You can set up min and

max number of samples, and adjust the amount of smoothingboth within the model, and

within the final output surfaces. You also can use a structural trend and/or a direction of

maximum continuity to force and mould the resulting shells.

Figure 3. The variogram set-up form, you can also select a triangulation to act as a

structural trend to drive the estimate.


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The whole process relies on Vulcans block modelling tools to build and then estimate the result

into the blockmodel as a distance function, grade shells are then created to generate the

output. If you are modelling categorical shells then the shells are meshed together to ensure

there are no holes in the model, and they are trimmed to the topography if that has been

selected. As the basic block modelling tools are used Vulcan is required to generate a BEF file

to control the estimate, and when run you get a standard validation report. Both these files are

written to the TMP file on your C: drive and are overwritten every time you create a model.

Although the BEF file is a standard file that you can open, edit and adjust it seems you cannot

re-run the estimate using the standard estimation process, although you can create distance

grade shells on the fields in the blockmodel. As an OK estimate it suffers all the foibles of a

standard OK estimate, some understanding of the OK estimation process is really required to

get a useful outputalthough it is very easy to generate a basic result.

So what about the results? I have taken the tutorial datasets I used for the Leapfrog-Micromine

comparison run them through Vulcan. As for the Micromine comparison; below I present the

same comparisons between Leapfrog (using Mining, but Geo outputs the same result) and

Vulcan.

Figure 4 shows the results of a basic Isotropic search of the copper variable from Leapfrogs

Marvin dataset. Its trying is probably as much as you could say for this isotropic result. It must

be remembered however that this a very basic no frills unconstrained OK estimatewhich for

anyone with a bit of experience in this field knows can be very full of pitfalls. With some trial

and error and some basic knowledge of the estimation processes and a study of the grade

distribution the output looks much better (Figure 5).


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Figure 4. Isotropic search on copper from the Leapfrog Marvin dataset, Leapfrog on the

top, Vulcan on the bottom.

Figure 5. Adjusted estimate using a basic search direction and adjusting the nugget

variable in the variogram.


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Figure 6 shows the results of using the Lithology modelling. Here like in the Micromine

comparison I have modelled the QzP rocktype from the Marvin Dataset. The final output shows

similarity between the two. In order to complete the Vulcan surface I have had to play with

block size and smoothing but the result is acceptable from a block model domaining point of

view. Figure 7 shows the total lithology modelling output when all 3 rocktypes have been

modelled. Again given the simplicity of this model the resulting wireframes are quite good and

are sufficient for domaining and modelling. More complex models are certainly possible but you

are constrained by the fact that you require the blockmodel to create the shellsand thus are

required to use an appropriate block sizes. As you are compiling a rocktype estimate and thus

have a dataset that is largely continuous (very low nugget) you can get away with blocks that

are smaller than you could if doing a grade estimate blocks of 5-10 times smaller than

drillhole spacing (ie 10-20m blocks on 100m spaced drilling) are acceptable, any smaller

though and you get artefacts in the wireframes. If you were doing a grade estimate you would

be hard pressed to defend blocks any smaller than drillhole spacing (25m for the 100m

spacing) and some would argue you should not go any smaller than (50m).


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Figure 6. QzP wireframe of the Leapfrog Marvin dataset, Leapfrog on the top, Vulcan on

the bottom.


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Figure 7. Setting up all the rocktypes in the IM form will build a solid model of the

geology, the output for simple geological models such as in the Marvin is quite

acceptable.

Figure 8 shows the results of modelling the 5gpt gold grade shell from Micromines NVG

dataset using the same anisotropy. From this angle it looks to be OK.


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Figure 8. 5gpt shell from the Micromine NVG dataset, Leapfrog on the top, Vulcan on the

bottom.

However, when looking normal to this you can see significant estimation errors (Figure 9). This

is a by-product of the OK estimation methods used by Vulcan. Playing around with all the

various estimation parameters, and the blocksize can reduce these errors but at the loss of

definition. A significant amount of the high grade does also get left out of the estimateagain

due to this being a standard OK estimate it may mean that blocks in the High Grade areas are

more smoothedand thus lower grade.


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Figure 9. Figure showing the Vulcan estimate normal to the plane of dip, search ellipse

artefacts arrowed in green.

Figure 10 shows the results of a largely unguided lithology interpolation of one of the NVG

veins, the results from both programs is unacceptable. Whilst I have used an anisotropy for the

search both programs have failed to adequately model the vein although the Vulcan option

looks more continuous it commonly includes/misses data it should not, at least the Leapfrog

program includes all the relevant data.


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Figure 10. Unguided vein interpolant of a single vein from the Micromine NVG dataset,

Leapfrog on the top, Vulcan on the bottom.

Figure 11 however, shows the results using a vein modelling approach. The Leapfrog surface

uses an interval selection and the vein modelling methods to drive the interpolation as

discussed in the Micromine comparison. With Vulcan I have used every guiding option I can

think of when using the interpolator, including using a structural trend surface and polygons

basically an explicit interpretation driving the implicit model. And played with all the various

input options, block size, etc. The result is far from acceptable. Although it has to be said that

Vulcan does have its own workflows for creating vein models by extracting the footwall and

hangingwall points and creating grids which are then meshed togetherbut these options donot make any use of the Implicit Modelling package which is what I am reviewing here.


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Figure 11. The same vein from the Micromine NVG dataset, Leapfrog using vein

modelling on the top, Vulcan using structural trend modelling and polygons on the

bottom.

In the final analysis, whilst the output for Vulcan is generally acceptable from a basic 3D

geological model generation point of view as demonstrated with the Marvin dataset it struggles

when handling the more complex geological architecture indicated in the NVG dataset.

Leapfrogs ability to handle complex datasets; its rapid shell generation, its true RBF method

and data analysis capability is a significant factor in its favour. Vulcan however requires quite a

bit more set up and planning getting to the same point, additionally some basic understanding

of kriging and its pitfalls is a requirement in getting a final end product.


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Modelamiento Implicito - VULCAN

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