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<encoded_tag_open />v Peter<encoded_tag_closed />Good morning or good afternoon everyone<encoded_tag_open />/v<encoded_tag_closed />
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from wherever you’re attending.
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I’m a professional geologist
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with a few decades of experience
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in mineral exploration and mining.
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And I’ve focused mainly on long-term
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mineral resource estimates in a variety of commodities,
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diamonds, base metals, copper, nickel, precious metals
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and PGEs and through a variety of deposit styles
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in North and South America and Asia.
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The bulk of my experience
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was spent at the Ekati Diamond Mine,
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where I contributed to a team that did the resource updates
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on 10-kimberlite pipes up there at the time.
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And I was with Wood, formerly Amec Foster Wheeler
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for nine years in the Mining $ Metals Consulting group.
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I’ve been at Seequent now since October, 2018,
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so just over two years
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and I am on the technical team
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supporting business development training
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and providing technical support.
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And I think a few of you
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will probably have communicated with me in that respect.
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And I do focus on the Leapfrog Edge
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resource estimation tool in Geo.
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So we’re going to cover
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basically the estimation workflow in Edge.
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So we’ll start with exploratory data analysis
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and compositing, we’ll define a few estimators.
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We’ll create a rotated sub-blocked block model.
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And this is kind of the trick for doing
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the grade-thickness calculation in Edge.
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We’ll evaluate the estimators, validate the results.
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Not thoroughly, but we will do a couple of checks
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and then we’ll compose and evaluate
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our grade-thickness calculations
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and review the results.
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And then of course,
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our work is intended for another audience
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and I’m picturing that the rotated
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grade-thickness model will go to
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engineers who will re-block it
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and use it for their mine planning.
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So I’m just going to stop my camera for now
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so that, there we go, turning it off and we’ll carry on.
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Now, this grade-thickness in Edge is one way to do it.
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We also have presented in the past,
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most recently at the Lyceum 2020 Tips &amp; Tricks
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with Sarah Connolly presenting
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and we’ll provide a link for you to this recording
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that was done last fall.
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So this is a way to do grade-thickness contouring in Geo,
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if you don’t have Edge.
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All right, now I’ll flip to the live demonstration.
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And we’re starting off with a set of veins.
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There are four veins here, quite a variety of drill holes,
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not a lot of sapling, but that’s kind of common
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with the many narrow vein situations
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because it’s difficult to reach them.
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Now let’s see what else we’ve got here.
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So that’s all of our veins and all of the drill holes.
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I’m going to load another scene,
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which will show us what we’ve got for vein 1.
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And it looks like I must have overwritten that scene.
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So I’ll just turn off some of these other veins.
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So here’s vein 1 with all of the drill holes.
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So let’s filter some of these for vein 1.
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So those are just the assays that we have in vein 1
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and it’s pretty typical that we’ve got clustered data,
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so they’ve really drilled this area off quite well
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with some scattered holes out around the edge,
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maybe chasing the extents of that structure.
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So in other words, we’ve got some clustered data here.
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So that’s a quick review of the data in the scene.
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Now I’m going to flip to just looking at the sample lengths,
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the assay interval lengths because those will help us
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in making a decision on what composite length to use.
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So this is the first of our EDA in the drill hole data.
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And I’m going to go to the original assay table.
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I do have a merged table that has the evaluated
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geological model combined with the assays,
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so we can do some filters on that.
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But let’s just check the statistics on our table.
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So at the table level, we have multivariate statistics
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and we have access to the interval lengths statistics.
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So looking at a cumulative histogram of sample lengths,
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we can see that we have about 80% of the samples
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were taken at one meter or less
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and then about 20 are higher
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and there’s quite a kick at two.
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So it looks like, well, if we look at the histogram,
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we’ll probably see the mode there,
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a big mode at one,
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but then it’s a little bumped down here at two meters.
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So given that we don’t want to subdivide
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our longer sample intervals,
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which would impact the coefficient of variation, the CV,
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it might make our data look a little bit better than it is,
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so we’ll composite to two meters.
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And that way we’ll get a better distribution of composites.
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And we are expecting to see some changes,
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but let’s look at what we’ve done for compositing.
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I have a two-meter composite table here.
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Just have a look at what we’ve done to composite.
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So I’ve composited inside the evaluated GM,
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that’s the back flag, if you want to think of it,
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the back flag models, compositing to two meters.
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And if we have any residual end lengths, one meter and less,
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they get tagged or backstitched I should say,
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backstitched to the previous assay intervals.
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So that’s how we manage that
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and composited all of the assay values.
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So we have composites.
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So let’s have a look at what the composites look like
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in the scene then, I think I’ve got a scene saved for that.
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No, I must have overwritten that one too.
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I was playing on here this morning
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and made a few new scenes,
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I must have wrecked my earlier ones.
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So let’s just get rid of the domain assays.
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Click on the right spot and then load the composite.
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So we have gold composites,
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pretty much the same distribution.
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And if I apply the vein 1 filter,
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we’re not going to see too much different either.
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Turn on the rendered tubes and make them fairly fat
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so we can see what we’ve got here.
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So there’s the composites.
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Now we do have, again, that clustered data in here
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and we have some areas where we had missing intervals
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and now those intervals have been,
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composite intervals have been created in there.
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So I think the numbers of our composites have gone up,
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but that’s a good thing really,
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because we’ve got some low values in here
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now that we can use to constrain the estimate.
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Well, let’s just check the stats on our composites.
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So we have 354 samples,
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with an average of 1.9 approximately grams
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and a CV of just over two.
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So it does have some variants in this distribution
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and it looks like we’ve got some outliers over here.
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I did an analysis previously,
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earlier on and I pegged 23 grams as the outliers.
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If I want to see where these are in the scene
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and you may know already that you can interact
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from charts in the scene, it applies a dynamic filter.
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So I’ve selected the tail in that histogram distribution,
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and it’s filtered now for those composites in the scene
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and I can see the distribution of them.
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Now, if they’re almost clustered,
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which means we might consider
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using an outlier search restriction on these,
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but I chose to cap them.
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Generally speaking, if you have a cluster of outliers,
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it may reflect the fact that there’s a subpopulation
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that hasn’t been modeled out, so you can treat it
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with a special outlier search restriction.
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I’ll just get my vein 1 filter back here.
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All right, so it was predetermined
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that we would use vein 1 as a domain estimation.
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So the next step in the workflow
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is to add a domain estimation to our Estimations folder.
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If you’re a Geo user, you won’t see estimations.
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It’s only when you have the Edge extension
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that you see this folder
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where you can define the estimations
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that you want to evaluate onto your block model.
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So let’s just clear the scene on this one.
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And I think I do have a scene ready to go for to this.
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So let’s go to saved scenes.
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Here’s my domain estimation using saved scenes,
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just saves a few clicks.
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Well, not a lot has changed from what we were looking at
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in terms of composites, but you’ll see now
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that where we had intervals in the drill holes before,
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now we have discrete points.
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So those reflect the centroids
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or the centers of the composites.
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And let’s have a look at, that’s the 3D view.
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And let’s just check that the stats still look okay.
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So go back up to the Estimation folder to the vein 1.
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I guess I can show you what the boundary looks like too.
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So I’m just double clicking on the domain estimation.
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It’s calculating a boundary block
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showing us the average grade inside the vein versus outside.
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And what we’re paying attention to here
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is what’s going on across the boundary.
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And in this case, there’s quite a sharp drop in grade.
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There’s a high grade contrast.
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So I will use a hard boundary.
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Now if this was more gradational,
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if the boundary was a little bit fuzzy, for instance,
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I could use a soft boundary and share samples
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from the outside to a specified distance.
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And this is calculated as perpendicular
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to the triangle faces, so it’s nearly a true distance.
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But I am going to use a hard boundary, just cancel that.
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And we’ll start looking at some of the other things here.
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So we’ve got the domain and the values loaded.
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I also did a normal scores transform
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in an effort to calculate a nicer variogram,
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but in the end, I didn’t go there.
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But we do have the ability to do
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a Gaussian Weierstrass
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or discreet Gaussian transformation,
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whatever you want to call it.
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Anyway, so it’s a normal scores transform,
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which can sometimes help to improve modeling,
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calculating and modeling variograms
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in the presence of noisy data.
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I opted instead, and we’ll go the correlogram here.
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So we’re looking now at our spatial continuity in our model.
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I opted to go with a correlogram
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because a correlogram will work better
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in the presence of clustered data.
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It is very good at managing to control outliers, noisy data,
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and it also helps to see past the clustered data.
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So the correlogram, it’s the covariance function,
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which has been normalized to the mean of the sample pairs,
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if you want to think of it that way.
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So the covariance function is divided by the,
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the mean squared of the data or the standard deviation
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of the data to normalize it to the mean.
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So we can see in our map
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that there is quite a strong vertical trend,
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the pitch actually is the vertical
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and the correlograms are displayed in their true form,
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which is inverted to how we usually display
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traditional semi-variograms.
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Anyway, so that’s why these curves are upside down.
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And I have managed to apply reasonable models, I think,
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to the experimental variograms, so if we look in the scene,
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we’ll see our variogram ellipse.
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It’s not very big, so the continuity isn’t great.
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But it does look reasonable together with the data.
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So that is the go ahead variogram.
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And the next thing we want to look at is declustering.
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So clustered data will give you
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typically an overstated naive mean of your samples.
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So we use different declustering methods.
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Leapfrog provides a moving window declustering.
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You can also use a nearest neighbor model,
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which gives you in effect like a 3D
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polygonal volume type of a weighting.
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So the samples are weighted by the inverse
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of the area that they have around them.
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And so these outline samples get more weight
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than the closely spaced,
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typically higher grade clustered data
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where we’ve drilled off the higher grade
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portion of the vein.
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And so the declustered mean
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is typically lower than your naive mean.
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So let’s have a look if that’s the case
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with our declustering tool in Edge.
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So here’s our distribution.
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And you can see as the moving window relative size
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increases, that the average grade comes down
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until it hits kind of a trough
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and then it goes back up again.
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And I suppose if you used a search ellipse
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that was the same size as your vein,
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it would come right back up to the input.
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So the input mean, and this mean is a little bit different
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than the 1.91 that we saw earlier.
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So this is the mean of the points,
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of the data rather than the length weighted intervals.
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So it’s just slightly different,
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but very close, 1.89, roughly.
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And if I click on this node,
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we’ll see that the declustered mean is 1.66.
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So file that number away for later.
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That’s our target.
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Moving on to estimators,
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and I defined three different estimators.
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There’s an inverse distance cubed,
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a nearest neighbor and a Creed estimator.
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And with the CV up around two,
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I was expecting that I would need
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a slightly more selective type of an estimator
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and that’s why IDCUBE.
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And we’ll see what the results are comparing the Creed
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to the inverse distance cubed.
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Now I did a multi-pass strategy as well,
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and that again, is to address the clustered data.
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So the first pass search,
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I’ll open up the first pass search here
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using the variogram and the ellipsoid
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is to the variogram limit.
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So it looked reasonable as a starting point
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for a first pass.
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And I have set a minimum number of samples of 14
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and a maximum of 20
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and a maximum samples per drill hole of five.
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That means that I need three holes on my first pass
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to estimate a block.
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And the block search isn’t very wide,
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so I’m trying to minimize the negative Creeding weights.
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So as the passes go, then the second pass uses a maximum
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or two holes to estimate the block.
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And then finally pass three, I’ll just show you,
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is pretty wide open, big search
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and no restrictions on the samples.
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So even one sample will result in a block grade estimate.
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So the idea here that this is just a fill pass,
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making sure that as many blocks as possible are estimated,
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and I use the similar strategies for the same sorry,
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same search in sample selection for the IDCUBE.
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So what does this look like when we evaluate it in a model?
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Well, I guess first off, we’ll build a model.
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Now I have one built already,
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but as I mentioned, the kind of the trick
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to doing the grade-thickness in Edge
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is to define a rotated sub-block model.
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So let’s see what that looks like.
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A new sub-block model,
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big parent blocks.
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The parent blocks are scaled to the project limits
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and it thinks that I need to have huge blocks
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because the topography is very extensive,
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but it’s not the case.
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Now I’m going to use a 10 by 10 in the X and the Y
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and the 300 in Z and when I’m done,
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I will have the, well, the next step actually
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is to rotate the model such that Z is across the vein
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and that way, with a variable Z
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going from zero to whatever it needs to be,
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it will make like an array of blocks
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that kind of look like little prisms.
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There’s a shortcut to set the angles of a rotated model,
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and that is to use a trend plane.
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So let’s just go back to the scene,
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align the camera up to look down dip of that vein
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and I’ll just apply a trend plane here.
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That’s got to be about 305.
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I’m just going to edit this,
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305 and the dip, 66 is okay.
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I don’t need to worry about the pitch for this step.
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It doesn’t come to bear
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when I’m defining the block model geometry.
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So now, I will set my angles from the moving plane.
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And now that model, it’s a little bit jumped off
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to the side there.
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It is in the correct orientation now
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at least for that vein, where am I?
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Oh, my trend plane isn’t very good.
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Let’s back up here.
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I’m going to define a trend plane first
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and then it’ll create a plane.
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To the side, 305
[00:19:37.140] 
and dipping, 66 I think was good enough.
[00:19:42.360] 
Now a trend plane and let’s get back
[00:19:44.905] 
to this block model business.
[00:19:46.380] 
New sub-block model,
[00:19:50.402] 
10 by 10 by 300.
[00:19:52.710] 
I want to make sure that I go right across the vein
[00:19:55.410] 
wherever there are any undulations.
[00:19:58.180] 
And I will just have five-meter sub-blocks.
[00:20:01.490] 
So the sub-block count two into 10
[00:20:05.070] 
gives me my two five-meter sub-blocks
[00:20:09.830] 
and let’s set angles from moving plane.
[00:20:15.890] 
It’s better.
[00:20:16.780] 
Now it’s lined up to where that vein is.
[00:20:19.455] 
There’s a lot of extra real estate here that we can get.
[00:20:24.480] 
We can trim that by moving the extents
[00:20:27.940] 
a little bit back and forth.
[00:20:30.928] 
Of course, the minimum thickness of that model
[00:20:34.030] 
in the Z direction is going to be 300
[00:20:36.370] 
because that’s what I have set my Z dimension to be.
[00:20:43.530] 
One more tweak and that’s roughed in pretty, pretty good.
[00:20:50.640] 
Of course, if you had an open pit,
[00:20:51.990] 
you would have to make it a little bit bigger,
[00:20:54.630] 
but this is going to be an underground mine.
[00:20:57.270] 
So that’s aligning it to the model
[00:21:00.830] 
and you can see by the checkerboard pattern
[00:21:03.090] 
that Z is across the vein.
[00:21:06.327] 
And then after that, I would set my sub-blocking triggers
[00:21:07.960] 
and devaluations and carry on.
[00:21:10.670] 
Now we already have a model built.
[00:21:12.410] 
So I’ll just click Cancel at this point
[00:21:16.735] 
and bring that model into the scene.
[00:21:19.805] 
Well, the first thing we could look at
[00:21:21.720] 
is the evaluated geological model.
[00:21:25.010] 
Oops, the evaluated geological model
[00:21:29.070] 
and that is filtered for measured and indicated blocks,
[00:21:31.940] 
but there’s the model.
[00:21:35.739] 
And if we cut a slice right along the model trend
[00:21:41.060] 
slice and cross the vein
[00:21:47.700] 
and then set the width to five and the step size to five.
[00:21:52.770] 
And this can be 125.
[00:21:55.820] 
Now I am looking perpendicular to the model.
[00:22:00.440] 
And if I hit L, on the keyboard to look at that model,
[00:22:07.040] 
I should be able to see those prisms
[00:22:09.840] 
that I was looking for.
[00:22:10.890] 
Yeah, they’re all kind of prisms.
[00:22:12.540] 
We can see this model isn’t very thick or tall,
[00:22:15.280] 
it’s only about five meters or less.
[00:22:19.940] 
And I don’t see any breaks in the block.
[00:22:22.290] 
So that means that my Z value at 300 is pretty good.
[00:22:26.260] 
If I would have used a Z at, let’s say a 100 meters,
[00:22:29.780] 
I may have had some blocks being split,
[00:22:32.300] 
but I want only blocks that are completely across
[00:22:36.454] 
that vein in the Z direction.
[00:22:40.960] 
So let’s turn off these lights, we’ve got our model
[00:22:44.360] 
and of course we’ve evaluated the GM
[00:22:47.560] 
and I set the sub-blocking triggers to the GM as well.
[00:22:51.890] 
Now I’m just going to my cheat sheet here
[00:22:53.750] 
to see what I also want to show you.
[00:22:56.580] 
So at this point, yeah,
[00:22:58.239] 
let’s have a quick look at some of the models.
[00:23:00.560] 
So that’s the geology.
[00:23:04.060] 
I evaluated,
[00:23:05.360] 
I created, sorry, I created a combined estimator
[00:23:08.170] 
for the three passes for the inverse distance cubed
[00:23:12.040] 
and the Creed estimator
[00:23:13.900] 
so that I can combine all three passes.
[00:23:16.810] 
Actually, I better show you what that looks like.
[00:23:19.180] 
So in the combined estimator,
[00:23:21.410] 
I just double clicked on it to open.
[00:23:23.320] 
I selected passes one, two, and three in order.
[00:23:26.528] 
It’s important because as a block is estimated,
[00:23:30.930] 
it doesn’t get overwritten by the following passes.
[00:23:34.700] 
So if I were to put pass three at the top,
[00:23:37.160] 
of course, everything would have been estimated
[00:23:38.680] 
with pass three and pass one and two
[00:23:40.610] 
wouldn’t have had any impact on the model at all.
[00:23:43.130] 
So yes, hierarchy is important and it is correct.
[00:23:48.210] 
So let’s just have a look at that model.
[00:23:51.690] 
So there’s the Creed, here’s the Creed model
[00:23:55.745] 
and it’s not bad, but I can see,
[00:23:59.420] 
I would have to tune it a little bit better.
[00:24:01.410] 
I think there’s some funny artifact patches of blocks
[00:24:04.870] 
and things, may or may not be able to get rid of those.
[00:24:11.520] 
And there are big areas around the edge
[00:24:15.370] 
that have just one grade.
[00:24:17.480] 
So that’s kind of reflecting
[00:24:19.518] 
the fact that there’s not a lot of data out there
[00:24:21.180] 
and that third pass search basically estimating the block
[00:24:24.490] 
with that one pass.
[00:24:26.670] 
Let’s see what the IDCUBE model looks like.
[00:24:31.000] 
That’s a much prettier model, I guess
[00:24:33.430] 
because the thing is how does it validate?
[00:24:37.195] 
And we will check it against the nearest neighbor model,
[00:24:40.750] 
which is kind of a proxy to a declustered distribution.
[00:24:44.150] 
Even though we will have more than one sample per block,
[00:24:48.280] 
it does kind of emulate or is a proxy for
[00:24:52.343] 
properly declustered to distribution.
[00:24:55.990] 
Anyway, let’s go back to the IDCUBE.
[00:24:59.260] 
Another thing that we can do,
[00:25:01.330] 
and that is to restrict our comparison
[00:25:06.490] 
within a reasonable envelope
[00:25:08.870] 
around the blocks that are well-supported.
[00:25:14.042] 
So this is basically, where does it matter?
[00:25:15.980] 
Like it doesn’t matter so much around the edges,
[00:25:18.580] 
we’re expecting a little bit of error out there.
[00:25:24.795] 
But if we define a boundary
[00:25:27.010] 
around that are of the well-drilled region,
[00:25:35.360] 
and it’s showing in there.
[00:25:38.190] 
There’s my well-drilled region.
[00:25:39.550] 
So I’m calling this my EDA envelope.
[00:25:42.050] 
I’m going to do my validation checks in that envelope.
[00:25:45.955] 
They’re going to be much more relevant
[00:25:48.745] 
than just having everything on the outside
[00:25:51.870] 
that is inferred confidence or less, let’s say.
[00:25:55.720] 
Okay, back to the model and let’s check our stats.
[00:26:01.090] 
So going to check statistics, table of statistics.
[00:26:07.505] 
And I want to replicate the mean of the distribution
[00:26:13.500] 
with my estimates.
[00:26:14.920] 
And you’ll recall that the declustered mean is 1.66,
[00:26:19.845] 
the nearest neighbor model is also very close to that
[00:26:22.600] 
within a percent, 1.689, and the CV is almost the same
[00:26:28.240] 
as it was for our samples, which was two.
[00:26:30.982] 
So that nearest neighbor model,
[00:26:33.116] 
again, reasonable proxy
[00:26:35.550] 
for the declustered grade distribution
[00:26:37.970] 
and very useful for comparison.
[00:26:40.040] 
The IDCUBE model comes in quite well.
[00:26:43.960] 
Well, it’s a little bit off, but not bad, 1.73, roughly.
[00:26:47.400] 
So we’re replicating the mean of our input distribution
[00:26:52.530] 
with the IDCUBE.
[00:26:54.530] 
For some reason, we’ve got higher grades
[00:26:57.470] 
in the ordinary Creed model than we do in our samples.
[00:27:02.450] 
So that’s kind of a warning sign
[00:27:05.000] 
and it is very much smoothed,
[00:27:06.810] 
it’s .65, a CV of .66, which is much less than two,
[00:27:11.800] 
so it’s probably overly smoothed.
[00:27:14.290] 
And without doing any additional validation checks,
[00:27:17.280] 
I’m going to use my IDCUBE as the go-ahead model
[00:27:21.160] 
and yeah, carry on from there.
[00:27:26.240] 
Now let’s see.
[00:27:27.220] 
Oh, I didn’t mention it,
[00:27:28.932] 
but yeah, I did do variable orientation.
[00:27:31.810] 
Funny how you can miss stuff when you’re doing these demos.
[00:27:36.270] 
I did do a variable orientation,
[00:27:38.730] 
which is using the vein to capture the dynamic
[00:27:44.720] 
or locally varying iroinite in the estimation,
[00:27:49.440] 
our implementation of variable orientation in Edge
[00:27:53.450] 
changes the direction of the search
[00:27:55.980] 
and the direction of the variogram,
[00:27:57.480] 
it doesn’t recalculate the variogram.
[00:27:59.200] 
It just changes the directions
[00:28:01.120] 
and applies that to the search
[00:28:02.980] 
so that we get a much better local estimate
[00:28:06.750] 
using the variable orientation.
[00:28:09.400] 
Okay, so moving right along,
[00:28:12.818] 
and the next thing is to get into the calculations
[00:28:15.600] 
because that’s where we do the grade-thickness.
[00:28:18.390] 
So I’m going to double-click on Calculations,
[00:28:21.030] 
it’ll open up my calculations editor.
[00:28:23.540] 
I better show you the
[00:28:29.910] 
panel with the tools
[00:28:32.600] 
Where are my tools?
[00:28:36.660] 
I’ll maybe open it in another way here.
[00:28:41.455] 
‘Cause I have to show you that panel.
[00:28:42.950] 
Calculations and filters
[00:28:53.082] 
so there should be a panel that pops out here
[00:28:56.280] 
that we can see the metadata that is used
[00:29:01.450] 
or the items we can select, go into the calculations
[00:29:04.450] 
and our syntax buttons.
[00:29:08.342] 
And isn’t that funny?
[00:29:09.953] 
It’s not being active for me.
[00:29:10.950] 
Well, let’s have a look at the calculations anyway,
[00:29:12.770] 
because the syntax is sort of self-explanatory.
[00:29:16.670] 
So I did do a filter for the,
[00:29:21.010] 
let’s expand, collapse that.
[00:29:23.092] 
So I did do a filter for my measured in indicated,
[00:29:25.970] 
which is within the EDA envelope.
[00:29:28.920] 
So that was my limits.
[00:29:31.360] 
I also did some error traps that found empty blocks
[00:29:37.470] 
and put in a background value.
[00:29:39.140] 
They didn’t get estimated.
[00:29:41.055] 
So if it’s the vein and the estimate is normal,
[00:29:44.320] 
then it gets that value,
[00:29:45.620] 
otherwise it gets a low background value.
[00:29:48.030] 
And I did that for each of my models.
[00:29:50.850] 
I also calculated a class, category calculation for class.
[00:29:56.450] 
So if it was in the vein and it was in the EDA envelope,
[00:30:00.610] 
and within 25 meters, then it gets measured,
[00:30:02.940] 
otherwise in the EDA envelope, it’s indicated.
[00:30:06.090] 
So I did contour
[00:30:09.990] 
the region of 25 to 45 meters
[00:30:14.860] 
and then drew a poly line.
[00:30:17.110] 
And that was what formed my EDA envelope.
[00:30:20.010] 
And then outside of that, if it’s inferred
[00:30:23.310] 
or if it’s still in the vein
[00:30:25.030] 
but beyond the indicated boundary,
[00:30:28.677] 
or my EDA envelope, then I just called it inferred.
[00:30:33.670] 
So there’s also, I did for the statistics,
[00:30:39.390] 
I did also create calculated,
[00:30:44.360] 
numeric calculation of the measured and indicated blocks,
[00:30:49.060] 
two those were just for more comparisons.
[00:30:51.900] 
But finally, finally, we’re getting to the thickness.
[00:30:55.490] 
So the thickness is pretty straightforward
[00:30:57.050] 
because we have access to the Z dimension.
[00:31:01.000] 
So all I had to do for thickness
[00:31:03.230] 
is say, if it was in the vein,
[00:31:05.230] 
then give the thickness model the value of the Z dimension,
[00:31:09.530] 
otherwise it’s outside.
[00:31:11.620] 
And then the next step after that
[00:31:14.350] 
is to do a calculation, very simple.
[00:31:18.170] 
If that block was normally estimated has a value,
[00:31:20.780] 
in other words, then we just multiply our thickness
[00:31:24.230] 
times the grade of that final model.
[00:31:26.330] 
And I used the IDCUBE model, and that’s that.
[00:31:30.170] 
So we can then look at these models in the scene.
[00:31:37.100] 
Any calculation that we do can be visualized in the scene.
[00:31:40.460] 
So let’s have a look there, see the thickness,
[00:31:42.820] 
so you can see where there might be some shoots
[00:31:47.620] 
in that kind of orientation.
[00:31:51.435] 
And if we multiply, sorry, grade times thickness,
[00:31:56.400] 
we can see, yeah, maybe there are some shoots
[00:31:59.280] 
that we need to pay attention to,
[00:32:01.870] 
maybe target some holes down plunge of these shoots
[00:32:06.530] 
to see exactly what’s going on.
[00:32:10.084] 
And as I mentioned, that model exists,
[00:32:14.280] 
well exists, we can now export that model
[00:32:18.780] 
to give it to the engineers.
[00:32:21.490] 
So let’s just go to our model.
[00:32:24.500] 
What does that look like?
[00:32:26.350] 
Export,
[00:32:29.260] 
let’s call it Sub-block Model Rotated,
[00:32:33.550] 
and we can export just the CSV file
[00:32:36.480] 
that has all of the information in top of the file,
[00:32:42.740] 
a CSV with a separate text file for that metadata,
[00:32:46.110] 
or just points if you just need the points
[00:32:48.460] 
for maybe contouring or something,
[00:32:51.010] 
but I’m going to select that CV output format.
[00:32:56.037] 
Well, I’ve already done this, so it is somewhat persistent.
[00:32:59.670] 
It remembered which models I had exported
[00:33:03.500] 
and then applying a query filters
[00:33:06.210] 
so I’m not exporting the entire model,
[00:33:08.650] 
just the one for vein 1
[00:33:10.910] 
and ignoring rows or columns
[00:33:14.730] 
where all of the blocks were in air condition or empty.
[00:33:18.170] 
And then I can use status codes, either his texts
[00:33:22.135] 
or his numerics, carry on here
[00:33:26.100] 
and pick the character set.
[00:33:27.260] 
The default usually works here in North America,
[00:33:31.687] 
and there’s a summary and finally export.
[00:33:33.440] 
There aren’t a lot of blocks there,
[00:33:34.570] 
so the export actually happens pretty quickly.
[00:33:37.970] 
So let me see what else I’ve got here.
[00:33:44.130] 
Yes, okay, so the filter,
[00:33:46.417] 
I just want to show you in the block model,
[00:33:48.290] 
I did define that filter for the vein
[00:33:50.980] 
1 measured and indicated.
[00:33:53.250] 
So that is kind of the view again,
[00:33:56.400] 
where the blocks are filtered for what matters.
[00:34:00.210] 
And that’s actually is, that’s the workflow.
[00:34:03.740] 
And I hope I’ve covered it in 30 minutes or less,
[00:34:07.970] 
and the floor is now open for questions.
[00:34:13.010] 
<encoded_tag_open />v Hannah<encoded_tag_closed />Awesome, thanks, Peter.<encoded_tag_open />/v<encoded_tag_closed />
[00:34:13.843] 
That was really good.
[00:34:15.670] 
I even learned a couple of things.
[00:34:18.504] 
I love when you sprinkle breadcrumbs of knowledge
[00:34:22.050] 
throughout your demos.
[00:34:24.240] 
Right, so we’ve got some time for questions here.
[00:34:26.220] 
I’ll give everybody a moment to type some things
[00:34:30.530] 
into that questions panel, if you haven’t done so already.
[00:34:35.010] 
I’ll start, Peter, there’s a couple of questions here.
[00:34:39.267] 
So the first one,
[00:34:41.217] 
how can you view sample distance on a block model?
[00:34:46.480] 
<encoded_tag_open />v Peter<encoded_tag_closed />Okay, maybe I’ll go back to Leapfrog<encoded_tag_open />/v<encoded_tag_closed />
[00:34:49.850] 
for that then.
[00:34:51.330] 
So sample or average distance and minimum distance
[00:34:56.000] 
are captured in the estimator.
[00:34:57.830] 
So let’s just go up to an estimator.
[00:35:07.217] 
Estimator, I’ll use the combined one.
[00:35:09.314] 
And in the outputs tab, if I want to see the minimum
[00:35:11.410] 
or average distance, I can select those
[00:35:14.140] 
as the output number of samples as well.
[00:35:18.354] 
So with that one selected,
[00:35:20.104] 
I should be able to go to my evaluated model.
[00:35:24.660] 
There’s my combined ID3 estimator, there’s average distance.
[00:35:29.637] 
So there’s a map of distance, to samples
[00:35:35.690] 
and each block, if I click on a block,
[00:35:38.070] 
I can actually go right to the absolute value.
[00:35:42.060] 
And you can export this too if you need that kind of thing
[00:35:46.400] 
in the exported block model.
[00:35:49.850] 
<encoded_tag_open />v Hannah<encoded_tag_closed />Okay, thanks, Peter.<encoded_tag_open />/v<encoded_tag_closed />
[00:35:52.140] 
Another question,
[00:35:53.210] 
how can I find that grade-thickness workflow
[00:35:57.900] 
on drill holes?
[00:36:00.150] 
I can actually just paste that into the chat here.
[00:36:02.800] 
I’ll paste that hyperlink.
[00:36:05.910] 
So that was our, we had a Tips &amp; Tricks session
[00:36:08.150] 
as part of our Lyceum. I’ll put that in the chat here.
[00:36:12.660] 
Okay, another question,
[00:36:13.780] 
we saw you pick your declustering distance in the plot.
[00:36:20.501] 
Is this typically how all
[00:36:21.520] 
<encoded_tag_open />v Peter<encoded_tag_closed />I missed the question Hannah.<encoded_tag_open />/v<encoded_tag_closed />
[00:36:23.414] 
<encoded_tag_open />v Hannah<encoded_tag_closed />We saw you pick your declustering distances<encoded_tag_open />/v<encoded_tag_closed />
[00:36:27.120] 
or distance in the plot, is that typically how
[00:36:30.010] 
all declustering distances are selected
[00:36:32.100] 
or can you speak more about declustering distances?
[00:36:36.400] 
<encoded_tag_open />v Peter<encoded_tag_closed />Well, generally speaking,<encoded_tag_open />/v<encoded_tag_closed />
[00:36:39.080] 
when we’re doing declustering,
[00:36:40.760] 
we’re targeting distribution
[00:36:44.750] 
where the area of interest has been drilled off more
[00:36:49.537] 
than outside and consequently,
[00:36:52.120] 
the naive average is higher than the declustered average.
[00:36:57.410] 
So that’s why I’m picking the lowest point here,
[00:37:01.940] 
the lowest mean from the moving window relative size.
[00:37:08.920] 
And so it’s,
[00:37:12.464] 
now that isn’t always the case.
[00:37:13.490] 
It could be flipped if you’re dealing with contaminants.
[00:37:16.250] 
In that case, you might find that your curve is upside down
[00:37:20.610] 
or inverted with respect to this one,
[00:37:22.680] 
and you would pick the highest one.
[00:37:25.080] 
So I have seen that a couple of times.
[00:37:26.880] 
I don’t have a dataset that I can emulate that,
[00:37:31.514] 
but this is typically where you’re picking
[00:37:34.735] 
your decluttering mean.
[00:37:36.160] 
Did that answer the question?
[00:37:37.840] 
<encoded_tag_open />v Hannah<encoded_tag_closed />I think so, yeah.<encoded_tag_open />/v<encoded_tag_closed />
[00:37:38.910] 
<encoded_tag_open />v Peter<encoded_tag_closed />Okay.<encoded_tag_open />/v<encoded_tag_closed />
[00:37:40.426] 
<encoded_tag_open />v Hannah<encoded_tag_closed />Another question just came in.<encoded_tag_open />/v<encoded_tag_closed />
[00:37:42.330] 
I know we’re past our time here,
[00:37:44.170] 
but I do want to squeeze these out
[00:37:46.210] 
for anyone who’s interested so.
[00:37:47.770] 
Thanks, great presentation.
[00:37:49.370] 
Is there a limitation to the model size
[00:37:51.980] 
for import or export using Edge?
[00:37:55.887] 
I guess we mean block model there.
[00:37:57.410] 
<encoded_tag_open />v Peter<encoded_tag_closed />Yeah, there doesn’t appear to be a hard limit<encoded_tag_open />/v<encoded_tag_closed />
[00:38:01.987] 
on most block sizes.
[00:38:04.010] 
However, I should qualify that the current structure
[00:38:07.875] 
for the Edge block model does not support
[00:38:11.120] 
the importing of sub-block models.
[00:38:13.980] 
So while you can export a sub-block model,
[00:38:15.980] 
you can’t import one, which is a bit of a limitation
[00:38:18.660] 
until we fully implement the octree structure,
[00:38:22.210] 
which is similar to some of what our competitors
[00:38:26.727] 
that use sub-blocked models as well.
[00:38:29.827] 
But I know there are people out there
[00:38:31.850] 
that have billion blocked block models
[00:38:35.277] 
that they’re working actively within their organizations,
[00:38:38.740] 
mind you they’re very cumbersome at that scale.
[00:38:43.490] 
<encoded_tag_open />v Hannah<encoded_tag_closed />Right, okay.<encoded_tag_open />/v<encoded_tag_closed />
[00:38:47.090] 
Well, that wraps up our questions.
[00:38:50.480] 
We’ve got another one who says thank you.
[00:38:52.787] 
You’re welcome.
[00:38:53.620] 
So thanks again, Peter.
[00:38:54.825] 
That was awesome.
<wpml_invalid_tag original=»PHA+» />[00:38:56.614] 
(gentle music)

Введение в Leapfrog Edge, динамический рабочий процесс расчета значений «содержание*мощность» (онлайн-обзор 2021 г.)

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