VOXI: constrained modeling (Remote Roundup 2021)

Start with the basics, and build up to Geologically constrained model.

Overview

Video Transcript

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(relaxing music)

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<v ->(Kanita) Okay, we’ll get started here.</v>

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So again, welcome to Seequent’s live demo

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of VOXI constrained modeling.

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My name is Kanita Khaled.

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And today we’ll be talking about

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how to incorporate constraints into your VOXI model.

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We’re going to keep things very practical today,

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it’s going to be a very hands-on overview

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on how we work our way up from an unconstrained model

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and then adding simple constraints,

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and then towards more complex geologically constrained model

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using drilling data.

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We won’t be covering too much theory,

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but we’ll start with the basics, and then work our way up.

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Okay, introduction.

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So my name is Kanita,

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I’m a geophysicist based here at North America,

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and today I’m joining in from Toronto.

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So my training, my background’s in geophysics,

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primarily in the mining and exploration field,

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and here at Seequent,

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I work within our technical team here in North America.

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Okay, so let’s dive right into the demo here.

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We’re going to jump into the application.

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I’m going to turn off my video here just to accommodate

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a little bit more bandwidth.

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Okay, so here I have an airborne magnetic dataset,

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flown over the Mount Palmer gold mine district

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in Australia.

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And this airborne magnetic data

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was collected at Hawaiian spacing of 25 meters spaced apart,

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And there are a total of approximately 35 lines

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of aeromagnetic data.

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I also have a digital elevation model, or typography,

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which is what we will be using for the inversion today.

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There has also been a drilling program for this project,

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and this drilling camp here has successfully identified

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two different iron formation zones

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that are associated with gold mineralization.

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So these iron formation meshes in magenta here,

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these are associated with gold.

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And still being able to map the geometry,

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and the extent of this banded iron formation,

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is very critical to this exploration program.

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And the purpose of the aeromagnetic survey was to be able

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to further delineate the geometry,

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and the extent of this banded iron formation.

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And really try to better understand

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whether these two interpreted units

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are separate (intelligible).

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is required to understand

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whether these are potentially connected has one unit.

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So our goal today is to work up,

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from an unconstrained model,

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to a geologically constrained model,

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using these drill hole lithology results,

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so that’s our goal.

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And to do that, we do start with our drilling data,

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and we do have to explicitly model these results

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so that we can work them into our inversion.

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So our first step to doing that,

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we have to carry out a process that’s known as wireframing.

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And wireframing is a form of explicit modeling

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of your geological data.

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You see this magenta body here,

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this magenta mesh here, or the iron formation.

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How did we get here?

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Well, through wireframing.

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So I do want to side step a little bit away from VOXI.

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I want to show you the wireframing process,

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because it is quite powerful.

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And the first step of wireframing, or drilling data,

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is to start off by creating cross sections.

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And you want to create cross sections

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that span your entire project area,

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so let me minimize this.

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And you can see that I’ve created

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quite a few cross sections here, I’ve done just that,

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and I’ve created several cross sections

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that span my project area.

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The more you have, the more cross sections you have,

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that you can use towards this wireframing process,

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the more detail your geological model will have.

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So, here’s an example of a cross section and I can,

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from here I can go ahead and start digitizing

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right on to this cross section.

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And to do that,

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we would be heading over the section tools,

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and creating a new geostring,

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we can give that geostring a name,

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and then we would have to add the features

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that we want to digitize.

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So here on the left, you can see two different units,

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you see the overburden, and you see that iron formation

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that we’re interested in, so we could add those in.

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So the overburden is alluvium, we can give it a color,

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and let’s call this overburden.

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And then similarly,

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we also want to digitize your iron formation,

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cause that’s where your gold is,

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and so you have to add that feature as well.

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I’ll call that sedimentary iron formation.

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And so now, I have these two features

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that I can then go ahead and start to digitize.

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And the digitization process

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is done right here on this cross section.

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Of course, in real life, in practice,

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I would do this a lot more carefully,

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but just for demonstration, that’s a very quick way

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to get go from having separate vocals and vocal apologies

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into a nice cohesive unit there,

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that’s been digitized right on the section.

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So in this manner,

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you want to do this for all of your sections,

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and let’s open up a more completed digitization process,

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just to show you what that looks like.

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So, here, now I have multiple cross sections

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within which I have my digitized bodies.

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So now that I have these digitizations on my cross sections,

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I have these nice features that connect my iron layers

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and my overburden layers,

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carrying out this process on all of my sections,

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I can then take it to 3D, and then

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wireframe it out into a cohesive body.

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Okay, so let’s close out these sections and head into 3D.

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So now I have my 3D view here.

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And if I were to bring in those interpreted digitizations

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into my 3D view, it would look something like this,

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turn off my drill hole data here.

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So here are those digitized bodies,

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right from that section now visualized in my 3D view.

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So the next step here would be to close the gap,

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between these disparate bodies, into one cohesive unit,

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and that is the process of wireframing.

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So to do that,

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I would select geosurface, wireframing,

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and then start wireframing.

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And starting the wireframing process

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would allow you to connect the dots,

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and come up with a cohesive unit

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that looks something like this.

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So I’ve got my overburden there at the top,

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and I’ve got my iron formation in magenta,

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here at the bottom.

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Okay, so that’s in a nutshell, what wireframing process is,

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and now these wireframing bodies, or meshes

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for the overburden here in blue,

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and the iron formation can be saved as a geo-subsurface file

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so that we could use it towards constraining our VOXI model.

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So, let me go ahead

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and open up a new project for my VOXI model.

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So, yeah, that was a bit of a crash course on wireframing,

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now we’ll take all of that

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and we’ll incorporate it within our VOXI project.

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So let’s start a new VOXI project,

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we’re going to start with an unconstrained model.

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So from the VOXI menu,

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we’re going to create a new project from polygon.

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You can give your project whatever name you like,

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and the polygon file here will be the file

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that outlines your area of interest here.

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And for your digital elevation model,

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you want to use the topography grid

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that we saw in the previous Reese’s montage project.

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So this is your Topo and the method we are using today

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is magnetics.

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And the model resolution. We want to keep this 10 meter.

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This is a,

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a good resolution for recovering some of the features that

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we want to see.

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And so this is going to create a new VOXI project.

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And next it’s going to ask me to add in my data.

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So, let’s say yes to that.

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And the database that we saw earlier,

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the one that contains our data, our mag data,

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we can pull that in, and Oasis will automatically,

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intelligently, read in the coordinate information,

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and you do have to specify your elevation.

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The model type we’re working with today

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is a susceptibility model.

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And the type of data we’re working with today

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is a magnetic dataset.

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And you do have to point the program towards which channel

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in your database contains the actual data.

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This is our residual magnetic intensity

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that’s ITRF corrected.

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So we’ll be using this,

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and we’ll go ahead and accept,

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we will go ahead and move a linear trend

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from the background and finish.

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So it’s simple as that.

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That is how you’re starting a VOXI project.

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You’re adding in the data.

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So here is our project space, or our model space,

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if you will. And this contains in the,

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in the small circles here,

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those are our observed data points,

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and those are placed within our model mesh.

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The model mesh is our model space within which our in

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version will, in-version results will converge.

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And on the left-hand side here,

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I can see a list here of constraints.

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I have one constraint that’s in bold.

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I’m just go ahead and turn that off for now.

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So these are all of the possible constraints that I can add

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to my VOXI inversion model before I press ready,

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But right now I don’t have any active constraints.

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If I did have an active constraint, that would be in bold.

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So right now I have none.

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So we could go ahead and run just this data as it is,

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without any constraints,

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and pressing run here would then essentially

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upload this data onto the cloud.

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This data would then run on the cloud and the results would

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then be downloaded onto my computer

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and into my VOXI project.

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And because this is running on the cloud,

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I could close my project up,

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work on something else, and then return to my project once

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it’s done.

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And so that really allows me to free up any computing power,

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as all the processing power is not really

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being accessed from my local machine.

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Okay. So for the sake of time, I have already hit run here,

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I’ve run the model,

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and I got my unconstrained model results.

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So let’s take a look at the unconstrained results from,

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from this aeromagnetic data set.

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So here is that wireframe body again,

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and I want to now look at my unconstrained susceptibility

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results versus our drilling results here.

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Okay. So here is the unconstrained susceptibility result,

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no constraints at all,

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and taking our, taking a first look at this,

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it’s promising,

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we can see that this pink anomaly, where my high is,

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we can see that our target is recovered,

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where it’s supposed to be, spatially.

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So we’re off to a good start.

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However, when I clip away at this, at this model here,

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when I clip away at the Y axis,

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to try to see how it corresponds,

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we see that the geometry is not really recovered.

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It’s a very smooth build,

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and it’s certainly not very compact.

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It’s not as compact as the target that I would be expecting

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for this particular project,

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but that’s essentially what an unconstrained result is.

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It’s giving me the smoothest possible results,

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so we can improve this.

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We can definitely add a little bit more known knowledge,

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before running our inversion,

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and that’s where this constraint tree comes into handy.

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This is where we’ll be adding our constraints from.

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Okay.

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So the first constraint we want to add is going to be the

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upper bound constraint. And that is exactly as it sounds

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we will right click and modify,

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and we’ll add an upper bound constraint of one.

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So what am I saying here?

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By setting an upper bound constraint of one,

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we’re saying that everywhere in this model,

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we want to limit our inversion results to a value of one.

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We don’t want to going higher than that,

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because we have that knowledge of this particular area.

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That’s what the walks in this area reflect.

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We’ll say yes to that.

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And similarly, you can do that for a lower bound.

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So this is our second constraint,

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and we’ll set this to zero, and now I have two things in

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bold, meaning they’re both active,

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and now we’ve applied two constraints, upper bound,

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lower bound, with the upper bound, we set a constraint,

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constant constraint,

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where anywhere on our project,

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the susceptibility value cannot be greater than one,

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that’s the upper bound. And anywhere on our project,

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our susceptibility cannot be less than zero.

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So in this way, it’s very subtle,

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but we’re guiding and pushing our solution towards values

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That make sense from a geological perspective,

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another really good and low effort

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constraint is the IRI focus.

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So this IRI focus is, again, like it sounds, it is,

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it’s a tool that allows you to focus in your results.

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It doesn’t require any prior knowledge of your geology or

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the geometry of your target.

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It stands for iterative, we’re waiting in version.

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And what it does is it just,

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it sharpens up an otherwise fairly smooth inversion

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result, like the one we saw,

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and it also improves any contact definition,

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so this is helpful for controlling the depth of your target.

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And it’s also very helpful in situations where you don’t

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know any prior geological information.

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So this is what we call a low effort, but high impact tool.

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And by default, I have this set to two,

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it’s a value that we know works quite well.

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Okay. So this is now in bold,

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so now I have three active constraints, an upper bound,

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lower bound, and then an IRI focus.

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The next thing is adding the geologic constraints.

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And where is my geologic constraint coming from?

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Well, it’s coming from this mesh that I created

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earlier on, this wireframed body.

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So this is what we now want to incorporate.

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Okay. So to do that,

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we’re going to be using the VOXI constraint builder.

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So from constraints, I’m going to select create,

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and then build a model.

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And we do need an input template

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that is going to be our mesh.

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This is exactly this mesh here that’s been exported out.

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And the constraint type here is going to be a parameter

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reference model,

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Excuse me, and for the contact here,

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we’re going to be using the geo surface file that we created

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from our drilling data.

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So we know we have that mesh. And from that mesh,

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we’re selecting the iron formation,

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and we’re setting outside this iron formation,

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anywhere outside this information,

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I’m setting a value of zero, and anywhere inside this

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formation, I’m setting a value of one.

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So essentially, I’m taking that surface and I’m seeing,

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and I’m guiding our inversion towards this particular value.

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And the beauty of the constraint builder is that you could

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keep adding more constraints, geological,

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geologic constraints, but for our purpose,

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we’ll just use the iron information as our main constraint.

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That’s going to go ahead and build that parameter reference

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model, which we can then see on our screen.

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And if I clip away at it,

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I can see that indeed it is that mesh. So we’re,

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we’ve now assigned value to that particular mesh from a

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physical property that we’ve assigned to that mesh.

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So with the parameter reference model,

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we don’t just supply the parameter reference.

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It’s also paired with a weighting,

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and this weighting allows us to define the confidence

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in this particular reference model.

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So that’s done through the parameter weighting here.

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I can right click and modify,

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and I’m setting this to a constant value of one.

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What does that mean?

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So this means that a value of one means that I have a very

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high level of confidence in this parameter reference model.

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And we have this level of confidence,

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because we know this is real drilled data,

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so we’re confident in it.

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You could also use ABOXOL, which is a 3d body.

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So that would then allow you to vary your

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level of confidence,

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if you had more confidence in certain areas

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versus the other, you could specify

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that through ABOXOL, as well.

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But for today, we’re going to say we’re very confident

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everywhere, and press okay.

[00:21:14.610]
And so now that is in bold,

[00:21:17.500]
so we now have the parameter reference with the mesh, and

[00:21:23.030]
we’ve given it a high weighting, or high confidence, of one.

[00:21:28.090]
Okay. Our last and final weighting today

[00:21:31.263]
will be the gradient weighting.

[00:21:36.000]
This, these three here, east, west, north, south,

[00:21:39.380]
and vertical gradient weighting.

[00:21:41.450]
So when you look at a drill core,

[00:21:44.570]
you often see really abrupt changes between two apologies or

[00:21:48.450]
your contacts, right?

[00:21:50.180]
So, you know, for example,

[00:21:52.040]
we know quite confidently that there’s likely a pretty sharp

[00:21:56.220]
contact between this iron formation and its surrounding

[00:21:59.300]
block units.

[00:22:00.980]
And if we wanted to reinforce those contacts

[00:22:05.780]
in every direction, east, west, north, south, and vertical,

[00:22:09.510]
we would have to apply the gradient weighting constraint.

[00:22:14.350]
And this constraint doesn’t require any knowledge,

[00:22:17.875]
or any information on physical properties.

[00:22:21.737]
You don’t need to know any SI values, or anything like that.

[00:22:25.140]
You don’t need any susceptibility values.

[00:22:26.850]
You’re just looking at the contacts,

[00:22:29.860]
and reinforcing the context, you’re sharpening up the edges.

[00:22:34.880]
Okay, so let’s go ahead and do that.

[00:22:36.940]
So we’re going to go into constraints, create,

[00:22:41.130]
and then gradient weight model,

[00:22:44.690]
and our input voxel is going

[00:22:46.974]
to be our parameter reference model.

[00:22:49.239]
That’s our, that’s the feature that you see

[00:22:52.190]
on the screen there.

[00:22:53.820]
And it’s asking me,

[00:22:55.520]
do you want to create this weighting in all directions?

[00:22:59.990]
You want to reinforce contacts in all directions, and I’ll

[00:23:04.340]
say yes to all directions and press okay.

[00:23:09.820]
And that would, then, go ahead and create separate waiting

[00:23:16.380]
voxels for each of those Cardinal directions.

[00:23:20.540]
So, we supplied a parameter reference model,

[00:23:23.810]
we supplied the weighting

[00:23:25.770]
for that parameter reference model.

[00:23:27.320]
We’re confident in it, and we’re saying,

[00:23:29.630]
take this reference model, and make sure you sharpen up all

[00:23:33.450]
of the contacts, and reinforce all of the edges in that

[00:23:38.110]
particular model. So, now we see these three now in bold,

[00:23:42.080]
meaning that they’re active.

[00:23:44.700]
So that was my last constraint, we have a total of

[00:23:48.682]
eight constraints,

[00:23:52.509]
eight constraints here.

[00:23:54.729]
Practically speaking,

[00:23:56.460]
I would encourage you to run the model each time you add a

[00:24:00.270]
constraint, and then inspect your result,

[00:24:03.252]
we’re going towards a solution,

[00:24:06.447]
Because if you add all of your constraints at once,

[00:24:09.930]
you’re not going to get a good understanding of how each of

[00:24:13.650]
these constraints are affecting your model.

[00:24:15.980]
So I recommend you to do this in steps, add a constraint,

[00:24:19.587]
run a model, add a constraint, run it again, evaluate.

[00:24:24.890]
And then I also recommend using the VOXI journal here to

[00:24:28.920]
track each step,

[00:24:29.810]
and keep a record of how you’re updating the model.

[00:24:35.045]
Okay. So I have all my constraints and I’m ready to run.

[00:24:40.350]
And just to be mindful of time,

[00:24:42.060]
I have run the results of this data set already.

[00:24:45.040]
So let’s go ahead and take a look.

[00:24:52.360]
So just as a reminder, this was our unconstrained model.

[00:24:58.040]
And now we can visualize the constraint,

[00:25:01.100]
constrained susceptibility result.

[00:25:06.070]
There’s our constrained model.

[00:25:09.034]
And, right away we can see that this,

[00:25:15.400]
that this model has a lot better,

[00:25:21.017]
it’s aligning with the geometry of my target a lot better.

[00:25:27.060]
And if I swing it around from this angle here,

[00:25:33.958]
I can see that it’s a lot more compact when compared to my

[00:25:36.930]
unconstrained model, right?

[00:25:40.020]
So again, there’s my unconstrained,

[00:25:42.530]
and there’s my constrained.

[00:25:45.040]
But when I start to inspect it further,

[00:25:47.980]
I do notice that there is this large volume here for the

[00:25:51.920]
susceptibility result.

[00:25:53.940]
And even after all of this constraints,

[00:25:57.472]
the susceptibility result show,

[00:26:00.330]
is showing us this large volume.

[00:26:02.380]
So, that kind of begs us to stop and question, and maybe

[00:26:06.940]
ponder whether these two bodies here

[00:26:11.421]
are potentially connected,

[00:26:14.650]
rather than the current geological interpretation,

[00:26:17.860]
which has these two bodies disconnected.

[00:26:21.710]
Even after adding our constraints,

[00:26:23.690]
we find that the theory that makes the most sense with our

[00:26:27.640]
geophysics is the one where these iron formations

[00:26:30.500]
are potentially connected.

[00:26:31.770]
And this is perhaps the time when you and your team might

[00:26:36.280]
wish to get together and discuss a new hypothesis for your,

[00:26:40.251]
for your geological model.

[00:26:43.730]
So here is our final model.

[00:26:46.572]
It led us to some new questions about our geological model,

[00:26:51.134]
and we added a total of eight different constraints.

[00:26:55.710]
So quite a rapid process with a lot of steps.

[00:26:59.950]
So I’d like to wrap up my presentation with that.

[00:27:04.340]
And I’d also like to thank you for your time.

[00:27:06.610]
If you have any questions about constraint building in VOXI,

[00:27:12.050]
I would be pleased to take them now.

[00:27:21.451]
<v ->(other speaker) Thanks Kanita.</v>

[00:27:22.390]
If anyone has any questions,

[00:27:23.430]
feel free to just type them into the chat panel.

[00:27:26.120]
I can see, we have one question already.

[00:27:31.973]
Do I need a separate extension

[00:27:34.870]
in Oasis montage for wireframing?

[00:27:39.340]
<v ->(Kanita) You do not need a separate extension in Oasis</v>

[00:27:42.530]
montage for wireframing,

[00:27:46.560]
or in target, for that matter.

[00:27:49.230]
You can wireframe right out of the 3d view.

[00:27:52.530]
Everyone who has OASIS montage has access to that, and

[00:27:56.300]
you can access it from geosurface and then wireframe.

[00:28:03.123]
<v ->(other speaker) Okay. Thank you.</v>

[00:28:05.900]
And one other question,

[00:28:10.540]
can I use drilling, or downhole data, to constrain a model?

[00:28:18.221]
<v ->(Kanita) Yes, you can use drilling data, definitely.</v>

[00:28:22.660]
So if you have either magnetic susceptibility,

[00:28:26.240]
if you’re doing a magnetic inversion,

[00:28:27.550]
or density data, if you’re doing a gravity inversion,

[00:28:32.320]
you can use those.

[00:28:33.900]
So if I go back to my VOXI model here,

[00:28:39.840]
if I go into constraints, create,

[00:28:42.650]
and then drill hole weight model,

[00:28:46.526]
this tool is what allows you to incorporate downhole

[00:28:51.540]
drilling, downhole geophysical data.

[00:28:55.160]
Something to keep in mind, when you’re putting in your

[00:28:58.340]
downhole geophysical data,

[00:29:00.460]
is that you’re measuring the absolute value

[00:29:04.690]
of the property.

[00:29:05.900]
Whereas VOXI is modeling contrasts in the property,

[00:29:11.250]
so you do need to consider whether maybe a mean background

[00:29:14.880]
would need to be removed,

[00:29:15.950]
and you can do that from this backgrounded mobile tool,

[00:29:19.420]
which we highly recommend.

[00:29:21.200]
But yeah, you can use down-hole geophysical data.

[00:29:30.791]
<v ->(other speaker) Thanks Kanita.</v>

[00:29:36.120]
At this point, I don’t see any other questions.

[00:29:42.480]
<v ->(Kanita) Okay, great.</v>

[00:29:44.390]
If you think of a question, after the demo,

[00:29:47.480]
or you have any questions in general about VOXI,

[00:29:50.750]
we’d love for you guys to get in touch,

[00:29:53.450]
you can reach me [email protected].

[00:29:58.170]
If you have any support, or workflow related questions,

[00:30:01.760]
[email protected] can also be reached.

[00:30:05.350]
And with that, I would like to end my presentation.

[00:30:07.540]
Thank you for joining and have a great weekend.