Implementation of streamlined data collection and viewing in early-stage exploration projects

Overview

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(light music)

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<v ->Hi and welcome everyone to Lyceum 2021</v>

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for our North American region.

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My name is Anne Belanger.

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I’m a Project Geologist with Seequent in Vancouver,

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and I’m pleased today to introduce James Rogers,

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who’s CEO and President of Longford Exploration Services.

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James is an exploration geologist

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who’s been working in our exploration sector since 2007

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and he’s worked in areas all over the world.

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Today he’s going to be talking about,

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“The implementation of streamlined data collection

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and viewing in early exploration projects.”

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James, would you like to start?

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<v ->Perfect, Anne thanks so much for the opportunity</v>

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to speak at Lyceum.

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It’s been an excellent adventure

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kind of getting started using the Leapfrog products

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along with all the supplementary software solutions

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over the last little while

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so today I’m hoping to talk a little bit about

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what that experience was like for us to onboard

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a couple of our projects

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as an early stage exploration company

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and dive into a little bit about

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how the resulting system has worked for us,

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its benefits and identify some of those challenges

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that we as early stage explorers often face

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and how we’re able to implement these solutions for us.

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A little bit about me, thank you for the introduction.

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So I do run an exploration services company

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called Longford Exploration.

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It’s grassroots-focused.

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We work around the world managing drill programs.

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Our specialty is kind of being those first boots

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on the ground, taking a project through its early stages

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of exploration and drilling

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and all the way up to that economic phase

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where we pass things off to groups with more engineering

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and financial experience than us.

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My background as an explorationist

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has been all across Africa, South America.

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I’ve managed and run projects all around the world

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and I think we’re up to 30, 40 countries

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here now that we’ve worked in

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so for a short resume of time

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we’ve accomplished a lot of things

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and we’ve certainly managed to take a look at

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a lot of projects and a lot of different data styles.

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And so one of the challenges that we’ve always had

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as we onboard either a new client

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or one of our own projects

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as we continue to pull things together

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is really coming up with data collection solutions

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that are quick to implement,

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that are able to accurately and bring together data

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across all historic work,

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all of our ongoing work

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and how do we bring everything together

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across an entire company solution

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so that our geologists are quickly trained,

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all of our data collection is done and streamlined

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and we can have oversight and accountability throughout it.

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So really what we are looking for

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and have looked for in a solution

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always comes down to money first,

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making sure that it’s a cost-effective solution

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for what we’re looking for.

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We want accountability so we know who’s entered that data,

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who’s the geologist that’s logged the core,

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who’s the geo-tech that’s shipped those samples off

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and how are we able to work through all of our errors?

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Oftentimes we’re working with data

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that has multi-generational legacy to it.

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Sometimes it’s paper drill logs back from the early 1960s

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or even sometimes further back

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and we need to be able to pull a lot of that historic data

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into some form of usable database

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that we can both keep continuity on it,

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but we can also keep visualization

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and be able to move the model

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and our project understanding forward.

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We need things that are quickly and easily customizable

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in the field,

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it’s very important for exploration

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and early stage exploration.

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We don’t have robust infrastructure at site often,

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we need to be able to quickly change and move our pivot

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or our pick tables for how we’re logging

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and collecting data and we need to make it efficient.

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We’re working long shifts collecting this data

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and the last thing we want to be doing is being frustrated

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at how we’re collecting it.

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So what was interesting for us

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as we went through a number of different solutions

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and we’ve tested a number of different collection softwares

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and management softwares,

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and really implementation and bringing things together

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really fast and coming up with a robust solution

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where all the pieces were talking to each other,

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it took about six years of trial and error.

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Earlier this year we onboarded a historic data set

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of about 50,000 meters of drilling

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across 172 drill holes going back to the 70s in this case,

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early 90s for the bulk of it,

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data was done and collected by various geologists

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and really sat around

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and hadn’t been pulled together and compiled.

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So this was the ultimate test scenario for us

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to kind of pull things together.

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So what we care about is how are we going to approach

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what I’m going to talk about today is,

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how do we look at the various elements of data

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that we need to collect

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and how are they going to all play together?

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So the first point is historic data,

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and that’s also old logs, old assay certificates,

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all of that information.

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The next part is the field data for our ongoing projects,

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that’s going to be the collar data

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of drilling point data for soils,

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all of the information about the field sites themselves,

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what kind of condition that drill sites are in.

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And then of course we’re going to bring our data collection

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into the core shack.

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So that’s going to be how are we logging it,

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how we’re imaging it, how we can make this more efficient,

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and when we’re reprocessing processing a lot of drill core

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it certainly takes a lot of time

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so the more efficiencies we can get in this

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the better we’re going to be able to move the data along.

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After we get that data collected

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and we of course want to have continuous stream

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of that data base itself evolving with both historic

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and new data that’s coming in,

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and we want to be able to push it out

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into the usable products,

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both for making decisions on the fly

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like visualizing where our drill hole progress is

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if we’re hitting our targets,

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and then moving it along into modeling

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and actually producing some final products on it.

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So starting first with the historic data itself.

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This is often a challenge for many projects

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that we first join and come into.

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You know we’re dealing with complicated access databases,

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multiple geologists of different exploration campaigns

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having different logging methods,

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maybe different logging codes.

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And there’s no way around it,

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we always end up having to do a lot of work in synthesizing

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and cleaning up this data.

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What really helped us in our final solution

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that we have here

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and what’s been our final solution

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for our current data collection is MX Deposit.

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So why that works well for us

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is we’re going to do a lot of legwork in Excel,

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we’re going to pull a lot of the logs together,

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a lot of that information down

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but then we’re going to be importing

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all of that historic data into MX.

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It’s going to identify the holes for us.

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It’s going to show us where our errors and our overlaps

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and where we have challenges

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but it’s going to give us the opportunity

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which is probably one of its most important features

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for dealing with historic data,

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is it’s going to allow us to pull information

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directly from original assay certificates

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and it’s going to give us a chain of data evolution

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so that we know that we’re working with the most up-to-date

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and the most correct data that we can.

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That means that our QAQC,

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if we’re onboarding a historic project,

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we’re actually going to be able to pull out and plot out

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how our QAQC standards and blanks were all the way back

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and not just be working with the resulting reports

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from the previous exploration campaigns.

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We’ll dive a little bit more into this as we go.

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This is an example of a historic data set

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that is pretty typical.

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This is in good shape,

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we’ve got a lot of different categories

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that have been defined,

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a lot of different attributes

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that have been pulled together

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and this is typically what we’re going to be wanting to build

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as we get ready to bring that data into MX Deposit

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for the import itself.

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Now when we bring in data into MX Deposit,

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it is incredibly fast for the amount of data

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that we can bring in.

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It’s going to give us some errors as we go often,

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as we bring in data,

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especially when it gets back to older things,

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it’s going to have a lot of missing intervals

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or perhaps missed keystrokes for sample IDs

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and this gives us a great opportunity to be able to flag

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and like identify where these challenges are

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so we can get back in and reference

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and we can actually remove or flag or add comment fields

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on to the data attributes themselves

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so that we know and we can rank

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sort of what that quality of data is,

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if we’re just going to be quantitative at best

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or if we’re actually going to be able

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to keep a qualitative data chain on any historic information

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as we bring it in.

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I’ll jump back in a little bit more on historic data

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as we kind of summarize at the end

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of some of the other advantages of MX

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and how the solution is working for us.

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But another important part that I want to talk about is

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the second part of our data collection,

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and that’s going to be at the drill.

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What we need to do

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and what we need to consider in a situation

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where we need to collect data at an actual drill site itself

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is that we need to be able to function offline

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without going over top of other files

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or other editing that may be going on.

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So MX gives us an opportunity to really pull out

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and check out a drill hole itself.

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We can go and make observations in the field

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on a handheld tablet, entering GPS coordinates,

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we can actually populate some coordinates right into it

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which is excellent,

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pop a picture in, whatever we might need to do

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that gives us the opportunity to really bring that data out

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to the drill rig itself

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or that data collector out to the drill rig itself.

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We’ve actually successfully implemented

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quick logging at a hole as well.

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Check a hole out where it’s important for deep holes

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when we’re shutting things down,

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it gives us an opportunity

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to make sure that we’ve got the surveys,

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downhole surveys, all populated,

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and we’re keeping track of all that data coming in.

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So our important parts for collecting data on this

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is it has to be field portable, it has to work offline,

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it has to sync when you bring it back in into the core shack

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or back into the field office

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and allow us to continue to move the project along.

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One of the great things with the way

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that we can work with MX is everything is very customizable.

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Everything’s very easy to make.

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We can start with the base template,

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but we can now start collecting attributes

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that we may only identify as being important

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once we’re in the field,

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that is maybe it’s whether casing is left in or not,

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or whether casing is kept.

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We are able to really pull together

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multi-generations of coordinates.

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We can have NAD 27 and then bring in NAD 83

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and we can rank what those coordinates are going to be

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that we want to use in our final project

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or even the local grid.

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So we’re yet start to be able to collect

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a pretty robust amount of data.

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A lot of the other solutions we looked at

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required 24, 48 hours worth of waiting time

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while we bring that project

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or that’s the problem to the actual coders

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behind the software itself

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and then they’re going to be able to build these fields for us.

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In the case of MX almost everything we can build ourselves,

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all the attributes and all the pick lists

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and that’s pretty important for a junior exploration company

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that’s nimble and has multiple projects,

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we really want to focus on data collection

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not on coding software on how to collect the data.

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A little bit more

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as we continue to look at the features of MX

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and I think this is an area

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that we’re going to continue to see advance

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within this solution itself

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but we’re able to start seeing

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and tracking how many sample dispatches,

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what our turnaround times are,

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how many samples we have in the full database,

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which ones are being accepted, which ones are not,

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how many drilled holes

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and really get a good picture and summary

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of what’s in progress in the logging process

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and what’s been completed, diversion tracking,

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and being able to see who’s done what inside of a project

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is pretty important for us as well

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and that introduces that accountability

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I spoke about earlier which is so important for us.

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I think as part of this solution,

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really the most important aspect

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is being able to efficiently

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and quickly collect accurate data in the core shack as well.

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We’ve ran into situations in the past with other software

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and other solutions where we maybe come across

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a new mineral assemblage

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or a new lithology that we weren’t expecting

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and you have to remember we’re not always drilling

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something that we have a great understanding on.

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And then, has again presented a challenge for us

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to be able to populate a pick list

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and give us that new lithology

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so the geologist that’s logging the core

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is oftentimes just entering that as a comment field

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and we rush through the job, we finish things up

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and now we’re dealing with this

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after we’ve already collected the data

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and we no longer have the rocks to look at

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right in front of us.

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And in MX, we just pop it right into a new pick list,

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publish it, it’s up and populated in minutes,

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and it gives us the opportunity to restrict that editing.

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So if we’re worried maybe we have a junior geologist

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or a younger geologist

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that hasn’t had a lot of experience on these rocks,

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often the tendency is to collect too much data

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or to break out too many lithologies

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which may not be important.

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So we’re able to kind of control how,

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and that forces our geologists and our teams

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to have a discussion about

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whether we should be breaking something out

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as a new lithology or otherwise.

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Some of the most time-consuming stuff in the core shack

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has always been recording recovery, RQD,

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the box inventories, magnetic susceptibility,

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specific gravities.

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So using tablets and using these systems

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we’re able to automatically calculate specific gravity

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just from punching in measurements

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directly from a tablet as we’re there.

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That gives us two very important things.

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One, it gives us a chance to make sure it makes sense

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and it’s real data,

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that we’re not making a data entry error,

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that the SG is lining up within some realms of possibility

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that makes sense.

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And it gives us that immediate database population,

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we’re not taking a clipboard back to enter data in.

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So MX Deposit is probably one of our biggest tools

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for collecting all of these things,

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all this data inside of the core shack itself as well

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right up to the sample dispatch.

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Its integration with multi-platforms

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whether it’s being on an Android tablet or on a PC

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is certainly useful for us as well.

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We’ve actually are able to collect or view data

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on a cell phone, to a wireless, like a weatherproof tablet,

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right up to that PC or desktop on the core.

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That gives us a lot of flexibility

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when we’re collecting data.

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Our geotechs are saving a lot less time during data entry

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which means we’re able to process more core

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and they’re also less fatigued with manual data

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being punched in at the end of the day

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from a spreadsheet or into a spreadsheet from clipboards

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or other means of recording.

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As I mentioned, we’re very much able to edit and manage

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our pick list here

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so if you come across a new alteration

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we’re easily able to describe it a new code and description,

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the same with lithologies.

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So these are customizable

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and very easily edited in the field.

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This is just an example of one of the projects

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that we’ve been working on here.

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We’re even able to color code.

[00:16:14.010]
There’s a little bit extra work working with the team.

[00:16:17.820]
We are able to color code these

[00:16:19.130]
so they’re accurately representing our colors in the model,

[00:16:22.220]
which is again,

[00:16:23.380]
just making that geologist’s and logger’s life

[00:16:25.880]
that much easier.

[00:16:29.130]
One of the next pieces of software that we really bring

[00:16:32.910]
to our solution that we’re currently employing here now

[00:16:35.870]
is Imago and this is an interesting solution

[00:16:38.560]
and we’d been looking at this for a couple of years

[00:16:41.370]
and we really started to deploy it earlier this year

[00:16:44.550]
on about a 10,000 a year drill program.

[00:16:47.100]
At first we were pretty reluctant

[00:16:48.540]
with how clunky a lot of the data collection had been

[00:16:51.680]
or the imaging collection and the viewers had been

[00:16:54.430]
but we’re pretty impressed with Imago.

[00:16:55.890]
It is web-based so you need an internet connection

[00:16:57.820]
to really upload and utilize it.

[00:17:00.150]
That being said, it is built in such a way

[00:17:03.620]
that it works extremely well

[00:17:05.010]
even on low bandwidth internet connections

[00:17:07.930]
which is excellent and incremental uploads

[00:17:10.380]
really helped us out with that.

[00:17:11.770]
So what we’re looking for

[00:17:12.720]
is to be able to really get good imagery

[00:17:15.430]
and be able to use it.

[00:17:16.630]
It’s one thing to have a file structure

[00:17:19.130]
that’s got 30,000 images of core in it

[00:17:22.040]
but that’s not really going to give us a lot of data.

[00:17:24.770]
So we really were wanting to leverage something that could

[00:17:28.710]
allow us to view our results,

[00:17:30.100]
kind of review what that core was looking like

[00:17:32.620]
and revisit our logs

[00:17:33.453]
without having to get back out to the core stacks,

[00:17:37.690]
especially when we’re four months waiting for results

[00:17:40.630]
as a lot of us were in 2020 and early 2021 here so far.

[00:17:47.110]
And Imago does integrate seamlessly with MX

[00:17:49.460]
and it’s a pretty impressive thing

[00:17:51.840]
so we’re able to go right from MX,

[00:17:53.480]
find a box that might have an issue,

[00:17:55.410]
or maybe a data overlap, go click on it, take a look at it.

[00:17:59.677]
We are building our own imaging stations

[00:18:02.610]
but they can be a little bit more robust

[00:18:04.340]
or as easy as we want to really capture this.

[00:18:07.140]
We are using some pretty,

[00:18:09.710]
I guess some pretty high tech camera equipment

[00:18:12.420]
with the tablet that’s all geared up.

[00:18:14.530]
On the left you can see sort of an instance

[00:18:17.180]
where we have a cart that we roll around from rack to rack.

[00:18:20.900]
We do dry, wet, carry on,

[00:18:22.850]
and a little bit more stationary on the right

[00:18:25.267]
and a bit more primitive of a solution

[00:18:28.290]
but we just moved the core boxes in here.

[00:18:30.770]
It’s very quick to meter it out,

[00:18:32.210]
it’s very quick to edit the photos

[00:18:34.760]
and that gives us a viewing option online

[00:18:37.120]
which has just been an incredible solution.

[00:18:39.480]
This has been great both for clients collaboration,

[00:18:43.210]
I’m able to coordinate a job or look at issues in core

[00:18:46.540]
or really identify and nail down

[00:18:48.200]
some of the things that might be going on

[00:18:49.550]
or our field team might be having troubles with

[00:18:51.420]
all the way, anywhere I am in the world

[00:18:53.440]
on a pretty limited internet connection

[00:18:56.730]
which has been excellent

[00:18:57.670]
and of course we can zoom in once we get our assays back,

[00:19:00.610]
see what’s going on.

[00:19:02.590]
We get an excellent amount of resolution,

[00:19:04.440]
that’s more of a hardware thing,

[00:19:05.820]
but the user ability of this,

[00:19:07.620]
of having that whole core shack altogether

[00:19:10.210]
is a big part of the solution

[00:19:14.960]
for how we want to treat and collect all of this data.

[00:19:19.270]
Once we get through all this MX Deposit

[00:19:21.760]
and what we’re collecting with Imago,

[00:19:23.640]
we’re wanting to get this up and visualized

[00:19:26.430]
as fast as we can.

[00:19:27.770]
So we’re dealing with a lot of drill hole data,

[00:19:29.500]
maybe our multi-surfaces, imagery, solids and meshes,

[00:19:32.440]
and we’re really looking for something

[00:19:33.600]
that wants a software solution here that’s quick,

[00:19:36.580]
that’s rapid, and we can push updates to it

[00:19:38.870]
and as we’re drilling

[00:19:39.703]
we can really get a sense of where we are

[00:19:42.570]
and what we’re seeing as fast as we can.

[00:19:44.510]
Time is everything on this,

[00:19:46.370]
the faster we can get our heads wrapped around

[00:19:49.260]
what we’re seeing,

[00:19:50.110]
the better job we’re going to be able to do for our clients

[00:19:52.410]
and be able to move our projects forward.

[00:19:54.880]
So we use a bit of a combination of Leapfrog and Central.

[00:19:58.700]
Leapfrog Geo is of course implicit modeling.

[00:20:02.460]
It gives us a lot of control over the data itself.

[00:20:05.700]
It’s very easy and simple to use.

[00:20:08.650]
Our field geologists can,

[00:20:10.280]
with very minimal knowledge about the program,

[00:20:12.030]
visualize, import and export viewers

[00:20:16.170]
and be able to really process the data

[00:20:18.010]
without even just using the modeling part proportions

[00:20:20.965]
of the software itself.

[00:20:22.610]
Of course, once we get into actual modeling

[00:20:24.660]
of solids and surfaces,

[00:20:26.810]
it’s an incredible software

[00:20:28.590]
and it’s very intuitive and easy to use.

[00:20:31.200]
It gives us the most nimble solution

[00:20:34.480]
for active exploration and visualization,

[00:20:36.980]
in fact, checking on all our historic data.

[00:20:38.983]
It’s an important part to mention

[00:20:40.800]
as well as we bring that data in,

[00:20:43.930]
we want historic data in particular,

[00:20:46.490]
we want to make sure we’re visualizing it

[00:20:48.040]
and it’s looking right and starting to make sense.

[00:20:50.050]
We’ve got drill holes pointing in the right direction,

[00:20:52.440]
and we have dips and azimuths recorded correctly.

[00:20:55.680]
We often end up having to revisit old logs

[00:20:59.930]
and make sure that we have captured things properly

[00:21:02.660]
where some projects might’ve been recorded negative dips

[00:21:06.510]
and others might have been positive dips

[00:21:09.720]
for, say, the inclination of a drill hole.

[00:21:12.690]
So that gives us a really good and quick and easy solution

[00:21:15.420]
to be able to verify that historic data

[00:21:18.270]
as we bring it all in

[00:21:19.570]
and start to build this robust database.

[00:21:22.350]
It will be the continuing factor

[00:21:24.120]
for us to be able to continue to visualize data

[00:21:26.620]
as it comes in.

[00:21:27.810]
Central gives us an opportunity of course to version track that

[00:21:31.580]
across multiple desktops

[00:21:34.430]
and then basically acts as a shared viewing platform

[00:21:38.680]
and a shared cloud-based solution for housing the projects,

[00:21:45.090]
the actual 3D projects as we move along.

[00:21:47.380]
Oftentimes we end up with multiple streams as we go,

[00:21:51.610]
multiple versions and branches of projects

[00:21:53.910]
as we go over time.

[00:21:56.110]
We might have a group modeling on resources

[00:21:58.900]
while we have another group importing historic data

[00:22:01.217]
and so keeping track of a lot of these models

[00:22:03.360]
is extremely difficult

[00:22:05.380]
and Central gives us the opportunity

[00:22:06.910]
to really work on that.

[00:22:10.360]
Now, of course, we’ve got all this data coming in

[00:22:12.210]
and we really want to work together

[00:22:14.240]
on how do we keep it cohesive?

[00:22:17.370]
How do we manage versions?

[00:22:19.220]
How do we keep everything moving forward

[00:22:21.550]
as we get more assays, as we get new data,

[00:22:24.340]
and how can we keep amending that database

[00:22:26.390]
with better geologic understanding

[00:22:28.010]
and more data as we collect it?

[00:22:30.890]
In our case as an organization

[00:22:33.150]
we often use cloud-based file servers like Dropbox

[00:22:37.340]
or file share and a number of other ones as well,

[00:22:40.070]
where we have our Central cloud-based

[00:22:43.250]
essentially all of our project data

[00:22:44.900]
from reports through to all of this active drilling data

[00:22:50.080]
down to whatever geochemistry interpretations,

[00:22:54.048]
other information that we have.

[00:22:56.300]
We find that it integrates seamlessly with any,

[00:23:00.050]
it could be an Amazon web-based server or Dropbox,

[00:23:02.470]
but we can integrate really quickly with their base projects

[00:23:04.693]
that we have them all locally stored.

[00:23:06.490]
All of our data is kind of backed up

[00:23:08.070]
and routinely exported outside of MX Deposit

[00:23:11.030]
into our local repositories

[00:23:13.180]
but our primary entry points for new data

[00:23:15.810]
become MX Deposit and all of the solutions like Imago

[00:23:21.630]
where we’re actually collecting the data in here.

[00:23:25.650]
This helps us keep QAQC validation all the way through.

[00:23:29.580]
We’ve got redundancy, so if we have to roll back in time

[00:23:31.997]
and take a look at things,

[00:23:33.170]
it’s very important for us

[00:23:34.370]
as part of that whole data management solution.

[00:23:37.820]
And Central, as I mentioned,

[00:23:39.250]
allows us to have multiple concurrent models

[00:23:43.160]
referencing the same data and move them lightly across

[00:23:48.080]
from whether it’s the field crew

[00:23:49.800]
to the corporate office back here in Vancouver

[00:23:51.760]
or wherever we need to visualize a project.

[00:23:55.070]
Another important part is how do we pull that data down

[00:24:00.160]
once we do have it collected

[00:24:01.570]
and MX Deposit gives us the ability

[00:24:03.630]
to generate a form of a drill log,

[00:24:06.570]
preserving all that header information

[00:24:08.470]
as well as the detailed logging descriptions.

[00:24:10.680]
Now this is important for us

[00:24:12.070]
because a lot of the softwares that we’ve used in the past

[00:24:15.280]
don’t readily do this

[00:24:16.640]
and it’s an important part of filing assessment work

[00:24:20.220]
on claims as well as reporting

[00:24:23.600]
as when we’re preparing assessment reports and final reports

[00:24:26.790]
from the work programs that we’re doing.

[00:24:28.300]
We need logs in this type of format,

[00:24:30.520]
tabulated data doesn’t work

[00:24:32.960]
for a lot of the government organizations

[00:24:35.760]
and for reporting, of course.

[00:24:40.400]
We’re also able to manage a lot of the QAQC.

[00:24:42.950]
As I mentioned earlier in the presentation

[00:24:45.050]
we can really reference original

[00:24:47.640]
and historic standards and CRMs

[00:24:49.640]
but we’re able to really bring in all of the parameters

[00:24:52.520]
of passing and failing for standards,

[00:24:56.360]
how we want to treat core duplicates

[00:24:58.180]
whether they’re solid cores.

[00:24:59.290]
We can rank how things look

[00:25:02.720]
in terms of what is the better assays, for example,

[00:25:05.870]
like a fire essay or for us a digestion,

[00:25:09.360]
we can really drill down

[00:25:10.510]
and start to populate our final data.

[00:25:12.870]
Now, when we’re dealing with the CRMs,

[00:25:15.370]
we can really quickly visualize pass and fail thresholds

[00:25:18.330]
and bring together a lot of quick and fast decisions

[00:25:23.020]
on rerunning things.

[00:25:24.190]
And as a project evolves and as we get more data,

[00:25:26.410]
we can start to update these parameters

[00:25:28.850]
for passing and failing, for standard deviations,

[00:25:31.320]
just based on our populations

[00:25:33.100]
that we’re getting from the lab themselves.

[00:25:35.800]
It’s extremely seamless to export from MX Deposit

[00:25:40.180]
as a table form.

[00:25:41.430]
We basically predefine an export

[00:25:44.460]
so that we’re essentially clicking one button

[00:25:46.450]
that is exporting our collar, our assays,

[00:25:49.171]
our various tables that we need to populate,

[00:25:54.383]
geomodel and continue to push that up into the cloud

[00:25:58.110]
so that we can use that across platform

[00:26:01.770]
or across different offices as I was mentioning.

[00:26:04.600]
So I think one area for improvement

[00:26:07.080]
is going to be having integration across these.

[00:26:09.370]
We want to see where right from MX

[00:26:11.510]
it’s feeding into the implicit modeling itself

[00:26:14.150]
as we’re collecting the data, flagging additional errors,

[00:26:17.650]
and being able to just that much more rapidly

[00:26:20.850]
update a model.

[00:26:21.800]
But certainly having that version of after a certain hole

[00:26:25.140]
or a certain date,

[00:26:26.320]
we’re able to capture data with a quick export

[00:26:29.170]
and really continue to efficiently collect

[00:26:33.080]
and visualize this data.

[00:26:40.070]
We’re also able to

[00:26:41.080]
within Central Manage our Licenses and Connectors,

[00:26:43.900]
this gives us a really interesting opportunity

[00:26:46.190]
to work with our clients.

[00:26:47.880]
Oftentimes they’ll have their own geologic teams

[00:26:50.130]
or just the executive team itself,

[00:26:53.110]
which you may want to have input on

[00:26:54.640]
how a project is progressing and how things are going

[00:26:57.240]
or just visualizing results as we get them.

[00:26:59.790]
Central allows us to quickly bring on other users

[00:27:02.950]
and we’re able to visualize and share things

[00:27:05.280]
in a very light branch of a model.

[00:27:07.200]
So we can actually share on a web-based viewer

[00:27:11.010]
to our clients or executive teams,

[00:27:14.320]
this is where we’re at, this is how this drill hole’s going,

[00:27:17.020]
we can add a comment, “We need to rerun some assay’s here

[00:27:21.700]
or let’s revisit these logs.

[00:27:23.440]
Lithology doesn’t seem to make sense

[00:27:25.150]
or maybe there’s a different interpretation than this

[00:27:26.960]
to go on.”

[00:27:27.980]
Central has been extremely useful for remote management

[00:27:30.510]
especially during COVID

[00:27:32.300]
where we don’t necessarily have

[00:27:34.140]
as many people on a site as we can

[00:27:36.340]
and travel has been more limited

[00:27:37.790]
but this has given us an opportunity

[00:27:40.050]
where I can view a project

[00:27:41.530]
and our field geologists can identify issues.

[00:27:44.540]
We can quickly click on a comment,

[00:27:46.850]
zoom right into an issue or problem,

[00:27:50.040]
a question or a point of just discussion that we might need.

[00:27:53.930]
It’s been well received for sure.

[00:27:55.800]
So in summary, looking at having a full solution

[00:28:00.880]
for early stage projects,

[00:28:02.500]
bringing in that historic data,

[00:28:05.180]
really we want to care about how easy and accurately

[00:28:10.240]
we can bring in historic data.

[00:28:12.730]
MX Deposit along with Excel and other access databases

[00:28:16.070]
has allowed us to accomplish that error checking

[00:28:19.670]
and most importantly referencing

[00:28:21.270]
original analytical software certificates

[00:28:24.660]
helps us reduce error and also increase the QAQC accuracy.

[00:28:29.800]
At the drill itself where we’re collecting

[00:28:37.030]
data right from the header itself

[00:28:40.270]
and Imago using that to collect imagery

[00:28:43.140]
that we can use to quickly view.

[00:28:45.620]
In the core shack itself

[00:28:46.750]
MX Deposit is logging directly into that.

[00:28:48.740]
Imago to capture and catalog all these photos

[00:28:52.080]
and then at the desktop using Central and Leapfrog

[00:28:55.150]
to both visualize view and continue to model

[00:28:57.840]
and build that understanding as we continue to go.

[00:29:00.870]
And then data management helps us

[00:29:03.380]
or the data management solutions such as Central,

[00:29:06.010]
will help us continue to track models as we go along.

[00:29:10.130]
I think that kind of concludes how we’ve implemented

[00:29:14.860]
these softwares to come up with a solution

[00:29:16.870]
for both historic data and ongoing data collection

[00:29:21.770]
and I’m appreciative of the time

[00:29:23.530]
to be able to tell you a little bit about

[00:29:25.260]
how we are implementing that in the industry.

[00:29:31.880]
<v ->Okay, great James, thanks so much.</v>

[00:29:34.050]
It was great to see

[00:29:35.310]
everything that Longford has been up to

[00:29:36.960]
and sort of how those options

[00:29:39.330]
are letting people certainly work together

[00:29:41.070]
including with your clients and even colleagues at site

[00:29:43.090]
to solve those problems quickly.

[00:29:44.380]
So thanks everyone for joining us as well.

[00:29:48.148]
If you have any questions

[00:29:50.230]
feel free to leave them in the chat.

[00:29:52.340]
We’re actually going to move along

[00:29:53.510]
to the next presentation

[00:29:56.060]
but we will follow up with those questions

[00:29:57.620]
within 24 to 48 hours

[00:29:58.990]
so just feel free to type them out if you have them

[00:30:02.360]
and thanks everyone for attending.

[00:30:04.653]
(light music)