When Your Data Speaks Volumes

Overview

Video Transcript

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<v ->Okay, hi there everyone.</v>

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As Ryan just mentioned, my name is Sarah Conolly

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and I’m part of the technical team

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here in Vancouver for Seequent.

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And today I’m going to be talking you though

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my tips and tricks and they are called

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“When your data speaks volumes”.

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I gave it pretty vague title for this one.

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If any of you know me from Geo,

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that could mean pretty much anything in there.

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But what we’re going to do today is

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take a look a little bit further down

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that mining value chain, so.

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Jeff and Anna were, kind of earlier on,

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Jeff found this bit for us, thanks Jeff.

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Anna cleaned up my data,

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and then I’ve actually got a model

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that has been built in Leapfrog Geo.

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I’m going to be moving closer towards my resource evaluation

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and I’m actually going to be focusing on my EDA,

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so my exploratory data analysis.

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Hopefully to show you a few little tricks

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using our interactive histograms to maybe think about how

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you could further refine your geological model

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and your estimation domains.

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So I’m going to jump into Geo.

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Excellent. Okay.

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So, here we’re in the same deposit

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that Anna and Jeff were looking at.

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We’ve got two massive sulfide lenses here.

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It’s a multi commodity deposit.

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So we’ve got copper, zinc, lead, silver and gold.

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My two massive sulfide lenses here

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have been built from the geology.

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So we’ve taken a look at our login information,

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we’ve found those massive sulfide in stalls,

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and we’ve created our volume.

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I am going to look to one of those volumes

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and just focus on this massive sulfide domain.

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So here I’ve got that volume of interest.

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I’ve got my data loaded and these,

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I’m looking at my zinc assays.

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But, I don’t understand the kind of statistics

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that lie behind this and whether or not I’m happy

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for my volume of interest to move forward

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into my resource estimation.

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So I’m going to take a look at a histogram of this data.

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Just make this a little bit smaller,

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so you can see them both. Okay.

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So I’ve done a few things before we got here today.

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I’ve merged my assay data with my geological information

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and I’ve created a filter so I can sort them

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looking at my values within this volume.

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And I guess when I was first kind of started working

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on grade control on models.

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The first thing that I was taught

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about my estimations of anything is that

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you’ve got to have consistent geology.

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I already told you that we have modeled this

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from the login.

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Obviously we validated that on like two photos

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and I’m happy with that side of it.

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Number two, that you’ve got one search orientation.

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You could maybe argue that

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there’s a little bit of folding in here,

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but for today we’re going to go with, yep.

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We’ve got that major twinge to our all body view.

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Number three, that we have a single population

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within that estimation domain.

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And if you take a look at my histogram on the right here,

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hopefully you can see we’ve got that

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kind of classic bell curve that we’re after.

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I’m fairly happy that I have one population.

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Maybe take a look on the right hand side of my histogram.

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You can see that around 25% of it

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we’re actually getting a bit of a breakdown

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in that population, and we’ve got a few outliers.

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How do I find those outliers? And what do I do with them?

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Do they cluster? Are they scattered?

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Do I need to rethink my estimation domain?

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Now I can build some filters to kind of try and find

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where that data is.

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However, in Leapfrog in the statistics tool,

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all you need to do is click on your histogram

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and those samples will appear in the scene.

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So now, if I kind of spin around with my volume,

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you can see those high grade outliers

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are actually scattered through my domain.

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And I’m probably more likely to deal with them with a

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top cut or maybe some restrictions during my estimation.

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So the zinc I’m pretty happy with to move forward.

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If we take a little look at the silver distribution,

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maybe we’ll see a different story.

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And depending on what view you look at this, these assays.

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The warm colors are representing the higher grade.

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The cooler colors are a lower grade.

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And hopefully you are seeing

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that we have a fairly sharp contact

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through the middle here.

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But let’s have a look at the statistics behind this as well.

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So I’m going to look at my silver histogram.

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And unsurprisingly, it looks like we’ve got

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a bi-modal distribution.

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So instead of a perfect kind of bell curve

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we’ve got a peak in the lower grades,

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and then another peak in the second grades.

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If we were to move forward with estimation

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do these samples really relate to each other? Maybe not.

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Maybe we’d want to sub domain this average

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to separate units.

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So again, these interactive histograms

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can be really handy to do that.

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So if I highlight the low grades, they gave me this scene.

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If I highlight the high grades,

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you get those intervals instead.

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Which is great.

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What’s the next step?

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We want to split them out

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and actually model them as separate units.

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So if we layer up this interactive histogram,

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with our interval selection tool.

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We can then create a refined model

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of our massive sulfide lens.

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So I’m just going to create a new interval selection.

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My silver domains.

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And again, I’m just going to re highlight the low grade

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samples and I’ll display them so you can see them.

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And then I just use my select all visible intervals,

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little button.

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And I’m going to assign these to a new category.

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So I’ll call this silver low grade.

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Let’s go back to that interactive histogram.

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Select the high grade.

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Select all those little intervals.

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And we’ll categorize these as our high grade.

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So I’m done with my histogram now.

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I’ll shut that down and save my interval selections.

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And we can display them in the scene.

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So now, rather than numeric data,

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I’ve re-categorized that into my low grade samples

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and my high grade samples.

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And the next thing that I’m going to do

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is actually use these to build another surface

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in the middle of my volume of interest.

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So if we go down to our geological models folder,

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we can create what are called refined models.

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And what they do, is that they’re basically going to take

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the volume from the first pass model,

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and then we can refine it like (undistinguishable)

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I’m taking this massive sulfide lens

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from my geology model.

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I’m going to look to my new intervals

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and create a new volume based off

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the selections that I just created.

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As I’m presenting I will create my volumes.

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To my surfaces I’ll just hide those drill hole intervals.

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So now we can see that within the volume of interest

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I’m splitting it by those two different domains I created.

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And as a final result,

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I could move forward with the two separate volumes

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on to my resource estimation.

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So that was my little tips and tricks for today.

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I was using my data to kind of build my confidence

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in my output volumes to move on to estimation

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at the end of the day.

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Thanks for listening.

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(crowd clapping)