Práticas recomendadas de modelagem numérica usando o Leapfrog Geo

We will take a deeper look at how Leapfrog Geo constructs a numerical model in order to build upon and provide a better understanding of the tools available at your disposal.

Video Transcript

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<v Suzzana>All right, hello and welcome everyone.</v>

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I make that 10 o’clock here in the UK.

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And so, let’s get started.

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Thank you for joining us today for this tech talk

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on Numeric Modeling elite for GA.

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By way of introduction, my name is Suzanna.

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I’m a Geologist by background based out of our UK office.

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And with me today are my colleagues,

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James and Andre both of him will be on hand to help

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run this session.

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Now, I run for this session is

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to refocus in on the interplant settings

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that will most effectively improve your numeric models

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from the word go.

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Let me set the scene for my project today.

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This is a copper gold porphyry deposit.

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And from the stats,

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we know that it is our early diorite unit

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that is a highest grade domain.

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For the purpose of today’s session,

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I’m going to focus on modeling the gold asset information,

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Some port from these drill holes, but in reality,

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I could choose to model any type of numerical value here,

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for example, our QT data.

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And if that is the workflow that you’re interested in,

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then I’d recommend you head over to the secret website,

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where there were a couple of videos

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on this topic already available.

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So coming down to my numeric models folder,

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I’m now going to select a new RBF interplant.

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And in the first window,

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start to specify my initial inputs and outputs.

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First, I’m going to want to specify

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some suitable numeric values.

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So in this case,

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I’m going to use my composited gold ISO table,

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but I can also choose to apply any applicable query filter.

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Now, in this case, as part of my initial validation steps,

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I actually created a new column in my collar table

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called valid simply to help me identify which drill holes

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I want to use in my modeling.

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I did this by first of all,

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creating a new category selection

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to make those initial selections

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then created a very simple query filter

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simply to exclude any of those that are invalid.

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And actually for the most part,

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it’s just this one hole at the bottom

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that I chose to exclude.

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As I work on my interpretation or indeed bring in more data,

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then it’s always useful to have flexibility

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like this built into your drill hole management workflow.

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So I would encourage you to set up something similar

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if not already done so,

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but for now though, let me just go back to my,

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I’ll be up in turbulent definition again,

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and let’s start to talk about the interplant boundary.

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Now, the default here is set to be our clipping boundary.

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And if you don’t know what that is,

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or if you want to set that,

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then you can actually do that from either the topography

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or indeed the GIS data folder.

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So I could choose to manually change this boundary

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or to enclose it around any object

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that already exists in my project,

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or indeed an existing model boundary or volume.

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Whenever I set here in my interpret boundary

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is going to be linked to the surface filter

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or by default is linked to the surface filter,

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which in itself controls which values can go into the model.

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I’m just going to bring a quick slice view into my scene,

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just to sort of help us visualize

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what I’m about to talk about.

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But I will explain this as this space

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is just a slice of my geological model.

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Just with those sort of gold values seen in a scene here.

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Typically your interplant boundary

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would be set to enclose the entire data set

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in which case all of your specified input values

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will become interpolated.

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So that’s kind of what we’re seeing in scene at the moment.

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It’s just a simple view of everything

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that’s in my data and in my project,

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I could choose however,

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to set a boundary around an existing volume.

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So for example,

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if I want to just specifically look at my only diorite

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Which is anything here in green,

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then I can choose to do that.

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And if I just turn that on now, then you can see,

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we’re just seeing that limited subset

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of input values in that case.

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Now, interestingly, if I wanted to add this point,

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mimic a soft boundary around the early diorite

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So for example, some sort of value buffer

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say it’s about 50 meters away,

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which is this orange line here.

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Then I could incorporate this also into my surface filter.

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And again, by doing, say,

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so if I now select this distance function here,

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then again, we’re going to see this update

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on which input values can be used into the model.

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But now though let’s not complicate things too much.

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I’m just simply going to use the same boundary

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as my geological model.

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And I’m also going to bring down my surface resolution

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to something a bit more reasonable.

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Now, a rule of thumb with drill hole data would be

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to set this to your composite length.

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So as to equal the triangulation

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or indeed some multiple of this,

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if you find the processing time takes too much of a hit

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for this particular data set,

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I have six meter lens composites already defined.

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So I’m going to bring surface ISO down

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to multiple of that 12 in this case.

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And of course those composites are there

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in order to help normalize

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and reduce the variance of my data.

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If I didn’t already have numeric complicit set up

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in my drill hole data folder,

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then I could actually go into find these here

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as well directly.

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But now though, let me just go back and use those,

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these composites that I have.

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And for now, let’s just say, okay to this and let it run.

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All right, now, there will be some times here

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that it’s going to be easier just to jump into models

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that have already been set up.

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And I think this is one of those cases.

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So let me just bring in that is going to generate,

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which is essentially our first pass gold numeric model.

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And if I stick the legend on,

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then you’ll start to get idea

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or reference point for that grade.

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Now, the power of leap frogs on behalf energy engine

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is in its ability to estimate a quantity

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into an unknown point by using known drill hole

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or point data.

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It’s important however,

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when we estimate those unknown points

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that we’re selecting an interpolation function,

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that will make the most geological sense

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at this first pass stage.

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I hope you’ll agree that we’re seeing something

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that’s very unrealistic,

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especially in regards to this higher grade sort of blow outs

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in my Northwest corner.

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Is fairly common for our projects

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to have some areas of data scarcity.

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If I bring my drill holes in here

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and just turn the filter off for a second,

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then I’m sure you agree that that sometimes at depth

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or indeed on our last full extents,

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that we might just not have any drilling information

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in that area.

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And in this case,

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it is the, it’s just three drill holes here.

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If I put my filter on,

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you can see that it’s just these three drill holes

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that is simply causing that interpolation here

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to become really quite extrapolated.

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And unfortunately there’s many an extreme example in line

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of such models.

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The C’s finding their way into company reporting.

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So what I would encourage you all to do,

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is to of course start refining the internal structure

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of my model and the majority of the functionality

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to do so actually sits under the interplant tab.

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So if I go to the model

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and I’ll go to the one that’s run individually,

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so just double click into it

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and I’m going to come to the interplant tab.

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For now, I’m simply going to change my interplant type

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to a splital and change my drift function to known

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I will come back to this in more detail, but for now,

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let me just let that run and we’ll have a look

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at what that produces instead.

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And again, great.

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Here’s one I prepared earlier,

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which we can now see,

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and hopefully you can already see

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this big notable difference already,

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especially where a high grade

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or where that original high-grade was blown out.

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This time, if I bring my drill holes back in again,

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we can see that high grade interpolation

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around those three drill hole still exists,

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but just with a much smaller range of influence,

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but why is that the case to help answer that question,

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I’m going to try and replicate these RBF and parameters

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on a simple 2D grid of points.

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And this should quite literally help connect the dots

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in our 3D picture.

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So let me move from this for a second

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and just bring in my grid of points

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and a couple of arbitrary samples.

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Hopefully you can see those values starting to come through.

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Here we go.

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So what I’ve done so far for this is,

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I have created three new RBF models

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in order to estimate these six points shown on screen here.

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And then I will come back into each of the Interplant tabs

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in order to adjust the interplant interests settings.

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And that’s just what the naming refers to here.

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Now, leap for geezers two main interplant functions,

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which in very simple terms will produce different estimates

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depending on whether the distance

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from our known sample points,

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the range is taken into consideration.

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And linear interplant will simply assume

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that any known values closer to the points

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you wish to estimate

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we’ll have a proportionally greater influence

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than any of those further away.

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A splital interplant on the other hand

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assumes that there is a finite range or limit

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to the influence of our known data

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beyond which this should fall to zero.

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You may recognize the resemblance here

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to a spherical very grim,

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and for the vast majority of metallic ore deposits,

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this interpolation type has more applicable

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exceptions to this, maybe any laterally extensive deposit

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like coal or banded iron formations.

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So in addition to considering the interpolation method,

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we must always also decide how best to control

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our estimation in the absence of any data.

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In other words,

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how should our estimation behave

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when we’re past the range of our samples,

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say in this scenario we saw just a minute ago

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with these three drill holes,

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but this, we need to start defining an appropriate drift

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from the options available.

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And I think that was the point that I sort of got into

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looking at on my grid.

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So at the moment I have a linear interplant type

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with a linear drift shown

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and much kind of like the continuous column bludgeoned

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than I have up here,

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we’re seeing a linear,

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a steady linear trend in our estimation.

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The issue is, it was the estimation around

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our known data points is as expected.

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The linear drift will enable values

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to both increase past the highest grade.

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So in this case,

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we’re sort of upwards to about a grade of 13 here,

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as well as go into the negatives past the lowest grade.

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So for great data of this nature,

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we’re going to want to reign that estimation in

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and start to factor in a range of influence.

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So looking now at our splital interplant

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with a drift of known,

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we can see how the range starts to have an influence

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on our estimation and then when we move away

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from our known values.

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So for example, if I start to come out

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onto the extents here, then the estimation is falling

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or decaying back to zero.

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And that will be the same

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if I sort of go around any of these,

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we would expect to be getting down to a value as zero

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away from any data points.

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And of course, if you are close to the data point,

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we would expect it to be an estimation similar.

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So where you’re modeling an unconstrained area,

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or perhaps don’t have many low grade holes

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to constrain your deposit,

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using a drift of known will ensure

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that you have some control on how far your samples

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would have influenced.

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That said, if you are trying to model something constraints,

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for example, the early diorite domain that we saw earlier,

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then using a drift function of constant

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could be more applicable.

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In this case, our values are reverting

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to the approximate mean of the data.

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So if I just bring up the statistics our mean here is 4.167,

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which means that as I’m getting to the outskirts,

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I would expect it to be coming back towards that mean.

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So a few different options, but of course,

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different scenarios in how we want to apply these.

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Now, if I jump back now to my gold model,

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then let’s start first of all,

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just with a quick reminder of where we started,

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which was with this model,

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and this is applying that default linear interplant,

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and that simply by changing this already

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to they splital or interplant type

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along with a drift of known,

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then we’re starting to see something

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that makes much more sense.

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So if I go back now and go back to my interplant tab

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to sort of look at some of these other settings.

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So far we know we want to limit the influence

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of our known data to a certain distance,

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and that it’s the distance reign

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essentially controlling that correlation.

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It’s reasonable, therefore that you’re going to want to,

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you’re going to want to change the base range

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to something more appropriate.

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And if you don’t know where to start,

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then a rule of thumb could be around twice

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the drill hole spacing.

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So in this case, let’s, that’s around 700,

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which is appropriate for this project.

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We also want to consider our nuggets and in leapfrog,

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this is expressed as a percentage to our some.

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Increasing the value of the nugget will create smoother

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results by limiting the effects of extreme outliers.

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In other words, we would give more emphasis

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to the average grades of our surrounding values

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and less on the actual data point.

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It can basically help to reduce noise

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caused by these outliers

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or with inaccurately measured samples.

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What value to use here is very much decided on a deposit

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by deposit case.

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And by all means, if you or someone else

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in your organization has already figured this out

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for your deposit, then by all means apply it here,

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perhaps for a gold deposit like this.

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Then a rule of thumb will be,

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let’s say, 20 to 30% of the sales.

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So let’s just take this down.

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For example, it’s 0.2, which is going to be 20% of that.

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So I have 10% might be more appropriate

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for other metallic deposits or indeed known,

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if you have a very consistent data set,

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we’ve spoken about drift already,

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but now that we are pressed the range of influence,

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then it’s really our drift that comes into play.

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Using known in this case is going to help

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control how far my samples will have an influence

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before the estimation to case back to Siri.

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I’m not going to delve too much

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into the other settings here.

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You can always read about these

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to your heart’s content online,

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but the take home point really is that,

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it’s the interplant the base range,

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that percentage nuggets to the sill and drift

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that will have the most material effects

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on your numeric model.

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Get these rights and you should be well on your way

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to producing a very best model

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that makes the best of your geological knowledge.

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All right, now we’ve talked a little bit about

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these interplant settings.

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I thought it would be worth a quick run over

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on the ISO surfaces.

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So we can see all these,

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as we expand out our new model objects,

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we’ve got ISO surfaces

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and then of course the resulting output volumes.

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When we pick cutoffs in our outputs tab,

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we are simply asking leap frog to define some boundaries

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and space and your contours,

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where there is the same consistent grade.

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Essentially the iso surfaces perform the exact same task

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as our geological surfaces do

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when we’re geologically modeling.

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But instead of building these surfaces ourselves,

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we’re simply picking grade boundaries

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that we want to see and leapfrog we’ll go

[00:22:20.330]
and build them for us.

[00:22:23.250]
It is this contour surface that,

[00:22:26.343]
that we’re seeing here in 3D.

[00:22:29.770]
And if we want to visualize a specific value,

[00:22:34.020]
then we can do so by updating the ISO values here

[00:22:40.760]
before I do that, let me just jump back

[00:22:44.850]
into the grid of points,

[00:22:46.980]
just to highlight this on a sort of simpler dataset.

[00:22:53.947]
So back in with my grid of points, let’s say for instance,

[00:22:58.810]
I now want to visualize a contour surface

[00:23:03.350]
with a greater two, then I can replicate that here

[00:23:07.612]
simply by setting some appropriate value filters.

[00:23:12.049]
And we can see that now

[00:23:14.520]
sort of finding all of these sort of values of two in space.

[00:23:18.746]
And these it’s this point that of course

[00:23:22.840]
is going to become our contour.

[00:23:27.900]
For the hikers amongst us

[00:23:29.401]
who are maybe used to seeing elevation contour lines

[00:23:32.550]
on a TD map.

[00:23:33.850]
This is the same principle,

[00:23:35.492]
except in the case of our gold model.

[00:23:38.180]
We’re simply looking at a value representation in 3D.

[00:23:43.060]
So let me go back to my gold model once again

[00:23:49.690]
and actually set some better ISO values for this deposit.

[00:23:57.710]
So am just going to go double click back in

[00:23:59.960]
and head over to the output tab.

[00:24:02.870]
So let’s just set a few, make a little bit more sense

[00:24:06.670]
than the ones that are put in as default.

[00:24:10.900]
So let’s say 0.5, 0.75.

[00:24:16.350]
Let’s do 1.25 and at 1.5, I think we’ll suffice.

[00:24:27.970]
The other thing that I’m going to do

[00:24:29.640]
is take down the resolution on my higher grades

[00:24:33.990]
to try and be as accurate as possible.

[00:24:38.190]
And remembering to know you might not have heard it,

[00:24:41.360]
but remembering my much earlier points

[00:24:43.650]
about matching this where possible

[00:24:46.010]
to the drill hole complicit lens.

[00:24:48.500]
So that’s why I’m sort of picking the six here.

[00:24:53.500]
There’s also the ability in this tab to clump output values,

[00:24:59.740]
the default assumption is that

[00:25:02.610]
nothing should fall low zero,

[00:25:04.320]
which is why we’re seeing that clump,

[00:25:07.151]
but you can always change that if need be likewise,

[00:25:11.440]
you can set different lower,

[00:25:14.710]
and indeed upper bounds in the value transform tab

[00:25:19.280]
to cap any values you may deem too low or too high,

[00:25:23.360]
but I’m not going to focus on that for now.

[00:25:28.110]
So let me just let that run

[00:25:37.030]
and again, in the magic of eight forklifts,

[00:25:42.510]
so have a look at, so what that has produced,

[00:25:46.940]
and again, we’re starting to see something visually

[00:25:50.630]
that perhaps makes more sense.

[00:25:52.710]
That makes more sense to us and to our gold boundaries.

[00:25:58.990]
Now, if I want to see more connectivity

[00:26:03.010]
between my drill holes, then aside from the base range,

[00:26:07.550]
I could use a trend for this.

[00:26:09.630]
Also the central shear zone, for example,

[00:26:15.720]
is no doubt playing a dominant structural control

[00:26:21.870]
on my mineralization.

[00:26:23.750]
Starting to see a lot of our high grades,

[00:26:25.490]
sort of following some sort of trend here.

[00:26:29.370]
So applying a structural trend should help

[00:26:32.400]
to account for any changes in the strength

[00:26:36.409]
and direction of continuity along this ridge.

[00:26:41.330]
I’ve already defined a structural trends for the shear zone.

[00:26:46.230]
I sort of put it as something like this for now.

[00:26:51.130]
However we could have caused always update this as need be.

[00:26:55.410]
Either way, this is a typical step to take.

[00:26:58.640]
Given that nature is very rarely isotropic.

[00:27:03.530]
So let me go and apply this structural trends to my model

[00:27:11.960]
and which I can do from my trend tab.

[00:27:17.790]
And I’ve only got the one in my project at the moment.

[00:27:20.220]
So it’s just that stretch or trend,

[00:27:25.240]
and let’s just let that run.

[00:27:34.000]
And again, in a fantastic here’s one I prepared earlier,

[00:27:43.430]
we can see what that has done

[00:27:45.370]
this definitely you can see how that trend has changed

[00:27:49.870]
there is weighting of points in space

[00:27:52.780]
to define that continuity along that ridge.

[00:27:57.550]
So though powerful and usually they’ll be applicable

[00:28:01.560]
certainly in our sort of metallic gold

[00:28:05.776]
or indeed any deposit.

[00:28:09.970]
So now that we’ve sort of run to this point,

[00:28:14.870]
it’s likely at this stage

[00:28:17.030]
that we’re going to want to hone in our mineralize domains.

[00:28:21.590]
I spoke earlier about the early diorites unit

[00:28:27.780]
having our containing our highest mean gold grade.

[00:28:31.650]
So let’s come full circle and create a numeric model

[00:28:36.439]
for just this for you also.

[00:28:41.430]
So let me clear the scene and let me cheat

[00:28:48.370]
by copying my last model

[00:28:51.640]
with all of its parameters into the new one.

[00:29:05.230]
And now we can go into that copy of our model

[00:29:11.880]
and start to apply reasonable premises here as well.

[00:29:16.750]
Now, the first thing we’re going to want to do is of course,

[00:29:20.060]
set some boundary at the moment

[00:29:21.470]
it’s just the exact same as the last one,

[00:29:25.900]
which is the extent of my geological model.

[00:29:30.760]
Say, let’s go in and set a new lateral extent.

[00:29:34.830]
And what I’m going to do is use the volume

[00:29:37.971]
for my geological model as that extends.

[00:29:41.290]
Say from surface, and then under my geological models

[00:29:48.370]
under the output volumes,

[00:29:50.200]
I’m going to select that early diorite.

[00:29:57.230]
Now, whilst that’s running.

[00:29:59.920]
Remember that what that is going to do

[00:30:02.960]
is first of all, constrain your model

[00:30:06.710]
to that unit of interest.

[00:30:08.340]
So this is my early diorite.

[00:30:12.530]
And it’s also just only going to use the values

[00:30:22.910]
that refer to this unit,

[00:30:26.320]
what to do with that initial surface filter set up.

[00:30:29.700]
We then of course,

[00:30:30.533]
want to go and review our interplant settings here

[00:30:34.120]
to check that they’re still applicable.

[00:30:35.693]
Now that we’ve constrained our data to one domain.

[00:30:40.740]
Let me say, let me go back and double click into this model.

[00:30:46.240]
And let’s just double check once again,

[00:30:47.788]
that our interplant settings make sense.

[00:30:52.570]
It’s probably going to be more applicable in this case

[00:30:58.635]
that where we have an absence of data.

[00:31:01.290]
So again, on the outskirts, on the extents of our model,

[00:31:08.460]
that perhaps we’re going to want to revert

[00:31:10.730]
to the mean of the grade in the absence

[00:31:13.610]
of any other information.

[00:31:16.110]
In which case, the best thing that we can do

[00:31:19.030]
is simply to update the drift here to constant.

[00:31:23.890]
Hopefully you remember from that grid of points,

[00:31:26.570]
how that is always going to revert to the mean of the data.

[00:31:31.200]
I think for now,

[00:31:32.740]
I’m just going to leave every other setting the same,

[00:31:35.460]
but of course we could come in

[00:31:37.630]
and start to change any of these,

[00:31:41.270]
if we wish to see things a little bit differently,

[00:31:44.340]
but for now, it’s that drift that for me

[00:31:47.070]
is the most important.

[00:31:48.570]
So again, let me rerun and bring in a final output.

[00:32:02.240]
And if I just make these a little less transparent,

[00:32:06.520]
you can hopefully see now

[00:32:08.230]
that whereas before we had sort of that those waste grades

[00:32:14.230]
coming in on this sort of bottom corner,

[00:32:17.260]
we’re now reverting to what?

[00:32:19.500]
To something similar of the mean and I think in this case,

[00:32:23.330]
if I remember rightly the mean is yeah, 1.151.

[00:32:29.272]
So we would expect to be kind of up in that sort of darker,

[00:32:33.130]
darker yellow colors.

[00:32:38.020]
At this point,

[00:32:39.620]
I’m going to say that I’m reasonably happy

[00:32:42.230]
with the models I have.

[00:32:44.720]
I would, of course, want to interrogate these lot further.

[00:32:47.900]
Maybe make some manual edits

[00:32:50.502]
or double check some of the input data,

[00:32:53.578]
but for the purpose of this talk,

[00:32:55.490]
I hope that this has gone some way

[00:32:57.910]
in highlighting which interplant settings

[00:33:01.090]
will most effectively improve your numeric models.

[00:33:04.984]
I appreciate that this is a very extensive topic

[00:33:08.450]
to try and fit into a shorter amount of time.

[00:33:10.550]
Please do keep your questions coming in.

[00:33:14.090]
In the meantime,

[00:33:15.130]
I’m going to hand over to James now,

[00:33:19.030]
to run us through the indicator RBF interplant tool.

[00:33:24.550]
<v James>Thanks, Suzanna,</v>

[00:33:25.730]
bear with me two seconds and I’ll just share my screen.

[00:33:28.850]
So a lot of the stuff that Suzanna has run through

[00:33:32.350]
in those settings, we’re going to apply now

[00:33:34.830]
to the indicator numeric models

[00:33:38.520]
indicating numeric models are a tool

[00:33:41.530]
that is often underused or overlooked in preference

[00:33:46.220]
to the RBF models,

[00:33:47.810]
but indicator models can have a really valuable place

[00:33:51.480]
in anyone’s workflow.

[00:33:53.870]
So I’ve got the same project here,

[00:33:55.910]
but this time I’m going to be looking at

[00:33:58.050]
some of the copper grades that we have in the project.

[00:34:02.120]
If I go into a bit of analysis initially on my data,

[00:34:06.310]
I can see that when I look at the statistics for my copper,

[00:34:09.968]
there isn’t really a dominant trend to the geology

[00:34:16.350]
and where my copper is hosted is more of a disseminated

[00:34:19.879]
mineralization that is spread across multiple domains.

[00:34:24.480]
If I come in and have a look at the copper itself,

[00:34:27.600]
what I want to try and understand

[00:34:30.170]
is what it would be a good cutoff to apply

[00:34:34.740]
when I’m trying to use my indicator models

[00:34:37.580]
and this case here,

[00:34:38.650]
I’ve had a look at the histogram of the log,

[00:34:42.470]
and there’s many different ways

[00:34:44.380]
that you can approach cutoffs.

[00:34:46.954]
But in this case, I’m looking for breaks

[00:34:49.440]
in the natural distribution of the data.

[00:34:51.640]
And for today, I’m going to pick one around this area

[00:34:54.600]
where I see this kind of step change

[00:34:56.593]
in my grade distribution.

[00:34:59.410]
So at this point here,

[00:35:00.900]
I can see that I’m somewhere between 0.28 and 0.31% copper.

[00:35:06.770]
So for the purpose of the exercise,

[00:35:09.950]
we’ll walk through today we’ll use 0.3 as my cutoff.

[00:35:15.051]
So once I’ve had a look at my data and I have a better idea

[00:35:18.210]
of the kind of cutoffs

[00:35:19.660]
I want to use to identify mineralization,

[00:35:23.020]
I can come down to my numeric models folder,

[00:35:26.720]
right, click to create a new indicator.

[00:35:30.710]
I’ll be up in turbulent.

[00:35:34.186]
The layout to this is very similar to the numeric modeling.

[00:35:39.010]
Again, you can see,

[00:35:39.843]
that I can specify the values I want to use.

[00:35:43.050]
I can pick the boundaries I want to apply.

[00:35:45.090]
So for now, I’m just going to use the title project

[00:35:48.700]
in all my data.

[00:35:51.420]
And if I want to apply my query filters,

[00:35:53.730]
I can do that here as well.

[00:35:56.086]
A couple of other steps I need to do,

[00:35:58.670]
because this is an indicator interplant.

[00:36:00.820]
I need to apply a cutoff.

[00:36:02.730]
So what I’m doing here is

[00:36:03.590]
I’m trying to create a single surface

[00:36:05.960]
in closing grades above my cutoff of no 0.3.

[00:36:11.840]
And for the purpose of time,

[00:36:13.240]
I’m also going to just composite my data here.

[00:36:16.490]
So that it helps to run,

[00:36:18.710]
but it also helps to standardize my data.

[00:36:22.770]
So I’m going to set my composite length to four

[00:36:26.400]
and anywhere where I have residual links, less than one,

[00:36:29.240]
I’m going to distribute those equally back through my data

[00:36:33.950]
can give it a name to help me identify it.

[00:36:37.350]
And we’re going to come back

[00:36:38.500]
and talk about the ISO values in a minute.

[00:36:43.520]
So for now, I’m going to leave my ISO value

[00:36:45.338]
as a default of no 4.5, and then I can let that run.

[00:36:52.460]
So what leapfrog does with the indicators

[00:36:55.580]
is it will create two volumes.

[00:36:58.090]
It creates a volume that is considered inside my cutoff.

[00:37:02.150]
So if I expand my model down here,

[00:37:06.440]
we’ll see there’s two volumes as the output.

[00:37:08.800]
So there’s one that is above my console,

[00:37:11.960]
which is this volume.

[00:37:12.960]
And one that is below my cutoff,

[00:37:15.890]
which is the outside volume.

[00:37:19.775]
Now, at the moment,

[00:37:20.690]
those shapes are not particularly realistic.

[00:37:23.560]
Again, very similar to what we saw

[00:37:25.330]
with Suzanna’s explanation initially

[00:37:28.720]
because of the settings I’m using,

[00:37:30.100]
I’m getting these blow outs to the extent of my models.

[00:37:35.020]
The other thing we can have a quick look at

[00:37:36.388]
before we go and change any of the settings

[00:37:38.940]
is how leapfrog manages the data

[00:37:42.550]
that we’ve used in this indicator.

[00:37:45.050]
So it’s taken all of my copper values

[00:37:46.850]
and I’ve got my data here in my models.

[00:37:49.340]
So if I track that on,

[00:37:54.503]
just set that up so you can see it.

[00:37:57.690]
So initially from my copper values,

[00:38:01.310]
leapfrog will go and flag all of the data

[00:38:04.230]
as either being above or below my cutoff.

[00:38:07.060]
So here you can see my cutoff.

[00:38:09.270]
So everything above or below

[00:38:12.420]
it’s then going to give me a bit of an analysis

[00:38:15.870]
around the grouping of my data.

[00:38:18.130]
So here you can see that it looks at the grades

[00:38:22.570]
and it looks at the volumes it’s created,

[00:38:25.060]
and it will give me a summary of samples

[00:38:28.870]
that are above my cutoff and fall inside my volume,

[00:38:32.477]
but also samples that are below my cutoff

[00:38:35.080]
that are still included inside.

[00:38:37.510]
So we can see over here,

[00:38:38.530]
these green samples would be an example

[00:38:43.010]
where a sample is below my cutoff,

[00:38:45.977]
but has been included within that shell.

[00:38:49.360]
And essentially this is the equivalent

[00:38:51.200]
of things like dilution.

[00:38:52.110]
So we’ve got some internal dilution of these waste grades

[00:38:55.130]
and equally outside of my indicator volume,

[00:38:59.210]
I have some samples here that fall above my cutoff grade,

[00:39:04.290]
but because they’re just isolated samples,

[00:39:06.240]
they’ve been excluded from my volume.

[00:39:08.610]
So with that data and with that known information,

[00:39:13.800]
I then need to go back into my indicator

[00:39:16.160]
and have a look at the parameters and the settings

[00:39:18.671]
that I’ve used to see if they’ve been optimized

[00:39:20.910]
for the model I’m trying to build.

[00:39:23.820]
What we know with our settings,

[00:39:25.880]
and again, when we have a look at the inside volume,

[00:39:29.840]
if I come into my indicator here,

[00:39:32.327]
and go and have a look at the settings I’m using,

[00:39:35.726]
then I come to the interplant tab.

[00:39:39.260]
And for the reasons that Suzanna has already run through,

[00:39:42.740]
I know when modeling numeric data

[00:39:45.950]
and particularly any type of grade data,

[00:39:48.020]
I don’t want to be using a linear interplant.

[00:39:50.544]
I should be using my splital interplant.

[00:39:54.157]
The other thing I want to go and look at change in is that

[00:39:57.380]
I don’t have any constraints on my data at the moment.

[00:40:00.410]
So I’ve just used the model boundaries.

[00:40:02.950]
So I want to set my drift to known.

[00:40:06.410]
So again, there’s more of a conservative approach

[00:40:09.060]
as I’m moving away from data

[00:40:10.207]
and my assumption is my grade is reverting to zero.

[00:40:16.860]
We can also come and have a look at the volumes.

[00:40:20.090]
So currently our ISO value is five.

[00:40:23.020]
So we’ll come and talk about that in a second.

[00:40:25.340]
And the other good thing we can do here is

[00:40:26.920]
we can discard any small volumes.

[00:40:31.130]
So as an example of what that means,

[00:40:33.670]
if I take a section through my projects,

[00:40:40.000]
we can see that as we move through,

[00:40:41.440]
we get quite a few internal volumes

[00:40:44.910]
that aren’t really going to be of much use to us.

[00:40:47.920]
So I can filter these out based on a set cutoff.

[00:40:52.690]
So in this case,

[00:40:53.523]
I’m excluding everything less than 100,000 units,

[00:40:57.540]
and I can check my other settings to make sure

[00:40:59.450]
that everything else I’m doing,

[00:41:00.720]
maybe I used the typography is all set up to run how I want.

[00:41:05.480]
Again, what Suzanna talked about,

[00:41:07.018]
was there any time that you you’re modeling numeric data

[00:41:11.055]
best practice would be to typically use

[00:41:14.180]
some form of trend to your data

[00:41:16.560]
so I can apply the structural trend here

[00:41:18.460]
that’s Susanna had in hers, and I can let that one run.

[00:41:24.400]
Now, when that’s finished running,

[00:41:27.700]
I’ve got my examples here.

[00:41:31.800]
So if I load this one on,

[00:41:36.320]
I can see the outline here of my updated model

[00:41:39.240]
and how that’s changed the shape of my indicator volume.

[00:41:44.810]
So if we come back out of my section, put these back on,

[00:41:54.160]
I can see by applying those,

[00:41:56.410]
simply by applying those additional parameters.

[00:41:58.270]
So changing my interplant type from linear

[00:42:01.060]
to splital adding a drift of known

[00:42:03.510]
and my structural trend,

[00:42:06.830]
I’ve got a much more realistic shape now

[00:42:08.620]
of what is the potential volume of copper

[00:42:12.460]
greater than 0.3%.

[00:42:17.320]
Now, the next step into this

[00:42:19.250]
is to look at those ISO values

[00:42:20.930]
and what they actually do to my models,

[00:42:23.900]
the ISO value and if we come into the settings here,

[00:42:28.322]
so I’m just going to open up the sentence again.

[00:42:31.030]
You can see at the end, I can set an ISO value.

[00:42:34.140]
This is a probability of how many samples within my volume

[00:42:39.280]
are going to be above my cutoff.

[00:42:41.830]
So essentially if I took a sample

[00:42:44.380]
anywhere within this volume,

[00:42:46.230]
currently there is a 50% chance that,

[00:42:48.690]
that sample would be above my no 0.3% copper cutoff.

[00:42:53.871]
So by tweaking those indicator ISO values,

[00:42:58.860]
you actually can change the way the model is being built

[00:43:01.760]
based on a probability factor of how many,

[00:43:07.110]
what’s the chance of those samples

[00:43:08.940]
inside being above your cutoff.

[00:43:12.020]
So I’ve created two more with identical settings,

[00:43:15.190]
but just change the ISO value on each one.

[00:43:18.710]
If we step back into the model as a on a section,

[00:43:26.640]
so here we can see our model

[00:43:28.600]
which going to take the triangulations off.

[00:43:30.960]
So we’ve got the outline.

[00:43:33.320]
So currently with this volume,

[00:43:36.650]
what I’m saying is that I have a 50% chance

[00:43:39.497]
but if I take a sample, anywhere in here,

[00:43:41.780]
it is going to be above my cutoff

[00:43:44.810]
can also change my drill holes here to reflect that cutoff.

[00:43:49.810]
So you can see here, no 0.3% cutoff.

[00:43:52.460]
So you can see on my drilling samples

[00:43:55.470]
that are above and below.

[00:43:58.840]
If I go into my parameters

[00:44:01.000]
and I change my ISO value down to no 0.3.

[00:44:06.060]
So we can have a look at the inside value on this one,

[00:44:10.790]
what this is saying and I just made some,

[00:44:12.890]
so you can see it as well.

[00:44:13.810]
It’s basically, this is a volume

[00:44:15.930]
that is more of a prioritization around volume.

[00:44:18.830]
So this could be an example.

[00:44:20.130]
If you needed to produce a mean case and a max case

[00:44:23.677]
and a mid case,

[00:44:25.150]
then you could do this pretty quickly by using your cutoffs

[00:44:28.620]
and using your ISO values.

[00:44:31.620]
So drop in an ISO value down to 0.3

[00:44:35.120]
is essentially saying that there’s a 30% confidence

[00:44:38.130]
that if I take a sample inside this volume,

[00:44:41.240]
it will be above my cutoff.

[00:44:43.240]
So you can see that changing the ISO value

[00:44:45.960]
and keeping everything else the same

[00:44:47.300]
has given me a more optimistic volume

[00:44:50.640]
for my indicator shell.

[00:44:53.180]
Conversely, if I change my indicator ISO value to 0.7,

[00:44:58.780]
it’s going to be a more conservative shell.

[00:45:00.660]
So here, if I have a look at the inside volume in green,

[00:45:06.150]
and again, maybe just to help highlight

[00:45:07.740]
the differences with these.

[00:45:11.100]
So now this is my, exactly the same settings,

[00:45:15.220]
but applying a ISO value.

[00:45:18.260]
So a probability or a confidence of 0.7.

[00:45:22.070]
And again, just to, for you to review the notes.

[00:45:24.730]
So increase in my ISO value

[00:45:28.640]
will give me a more conservative case.

[00:45:31.720]
This will be prioritizing the metal content,

[00:45:34.500]
so less dilution.

[00:45:36.520]
And essentially I can look at those numbers and say,

[00:45:38.720]
I have a 70% confidence that any sample inside that volume

[00:45:42.700]
will be above my cutoff.

[00:45:47.020]
So I say it very quickly,

[00:45:48.520]
and particularly in the exploration field,

[00:45:50.610]
you can have a look at a resource or a volume.

[00:45:53.700]
And if you want to have a look at a bit of a range analysis

[00:45:56.300]
of the potential of your mineralization,

[00:45:58.500]
you can generate your cutoff and then change your ISO values

[00:46:03.340]
to give you an idea of how that can work.

[00:46:07.220]
Once you’ve built these,

[00:46:08.090]
you can also then come in and have a look at the,

[00:46:10.050]
it gives you a summary of your statistics.

[00:46:12.830]
So if we have a look here at the indicator at 0.3,

[00:46:16.040]
I look at the statistics of the indicator at 0.7,

[00:46:22.254]
we can go down to the volume.

[00:46:24.840]
So you can see the volume here

[00:46:27.610]
for my conservative ISO value at 0.7

[00:46:31.900]
is 431 million cubic meters,

[00:46:34.640]
as opposed to my optimistic shell, 545.

[00:46:40.330]
You can also see from the number of parts.

[00:46:42.350]
So the number of different volumes

[00:46:43.840]
that make up those indicators shells in the optimistic one,

[00:46:48.310]
I only have one large volume,

[00:46:50.930]
whereas the 0.7 is obviously a bit more complex

[00:46:54.520]
and has seven parts to it.

[00:46:57.314]
The last thing you can do

[00:46:59.190]
is you can have a look at the statistics.

[00:47:02.180]
So for example, the insight volume,

[00:47:04.720]
I can have a look at how many samples here

[00:47:06.970]
for below my cutoff.

[00:47:09.220]
So out of the what’s that 3,465 samples,

[00:47:15.260]
260 those are below my cutoff.

[00:47:19.200]
So you can kind of work out a, again,

[00:47:21.613]
a very rough dilution factor by dividing

[00:47:24.100]
the number of samples that fall inside your volume

[00:47:28.310]
by the total number of samples,

[00:47:29.610]
which would bring your answer around

[00:47:31.020]
in this case for the 0.3 around seven and a half percent.

[00:47:37.580]
So it’s just, this exercise is really just to highlight

[00:47:40.610]
some of the, again, the tools aren’t necessarily

[00:47:43.162]
used very frequently,

[00:47:45.200]
but can give you a really good understanding of your data

[00:47:48.930]
and help you to investigate the potential of your deposits

[00:47:53.200]
by using some of the settings in the indicator templates,

[00:47:58.669]
but ultimately coming back to the fundamentals

[00:48:02.620]
of building numeric models.

[00:48:04.870]
And that is to understand the type of interplant you use

[00:48:08.530]
and how that drift function can affect your models

[00:48:11.750]
as you move away from data.

[00:48:15.160]
So that’s probably a lot of content for you

[00:48:18.822]
to listen to in the space of an hour as always,

[00:48:23.500]
we really appreciate your attendance and your time.

[00:48:26.680]
We’ve also just popped up on the screen,

[00:48:28.580]
a number of different sources of training and information.

[00:48:33.810]
So if you want to go

[00:48:35.347]
and have a look at some more detailed workflows,

[00:48:38.440]
then I strongly recommend you look at the Seequent websites

[00:48:41.980]
or our YouTube channels.

[00:48:43.500]
And also in the, MySeequent page,

[00:48:45.930]
we now have all of the online content

[00:48:47.860]
and learning available there.

[00:48:50.110]
As always we’re available through the support requests.

[00:48:52.660]
So drop us an email

[00:48:54.057]
and if you want to get into some more detailed workflows

[00:48:57.820]
and how that can benefit your operations and your sites,

[00:49:01.830]
then let us know as well.

[00:49:03.570]
We’re always happy to support

[00:49:05.420]
via project assistance and training.

[00:49:08.900]
So that pretty much brings us to the end of the session

[00:49:13.290]
to the top of the hour as well.

[00:49:14.480]
So again, thanks to you for attending,

[00:49:17.780]
thanks to Suzanna and Andre for putting this together

[00:49:21.940]
and running the session.

[00:49:23.500]
And we’ll look to put another one

[00:49:25.700]
of these together again in the new year.

[00:49:27.690]
So hopefully we’ll see you all there.

[00:49:30.630]
Thanks everybody and have a good day.