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Validation is integral part of the geological modelling process to ensure a robust model.

With Leapfrog Geo and Central you can create fully integrated workflows for your team. Add data or revise your interpretations at any stage, and these changes flow downstream, with no additional configuration (scripting/programming) required, for rapid updating, validation and efficient decision making.

Validation is integral part of the geological modelling process to ensure a robust model. With Leapfrog Geo and Central you can create fully integrated workflows for your team. Add data or revise your interpretations at any stage, and these changes flow downstream, with no additional configuration (scripting/programming) required, for rapid updating, validation and efficient decision making.

Join Seequent’s Jillian James (Project Geologist) to discuss:

  • Validating your drillhole database
  • Visual validation issues
  • Using Leapfrog’s statistical tools to validate your model
  • Using Query Filters to test areas of your model
  • Upload to Central for Peer review and final validation



Jillian James
Project Geologist- Seequent


23 min

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Video Transcript

<v Jillian>Hi, everyone.</v>

Thank you for taking the time to join me today

for this Tip Tuesday on validating your Leapfrog Geo model.

My name is Jillian James,

and I’m a project geologist here in Seequent’s Perth Office.

Seequent has a range of software

for use throughout the exploration and mining value chain.

Today, the focus will be on the functionality

in Leapfrog Geo with a brief introduction

to Seequent Central.

The key aims of this session

are to give you some tips and tricks

on how to validate your Leapfrog Geo model

and make validation an integral part of the modeling process

rather than just an afterthought.

In this session, we’ll cover:

how to check the data feeding into your model is validated,

tips on section by section validation,

how you can use Leapfrog Geo statistical tools

to validate your model,

how you can use query filters to test areas of your model,

for example, if you want to test where the model was barren,

but the grades are above a certain value,

where the model was mineralized,

but the grades are below a certain value,

and where the geology log does not match the model geology.

Finally, we will look at how to upload your project

to Central for peer review and final validation.

The most important step in validating any geological model

is to check that the data

feeding into the model is validated.

Remember, rubbish in equals rubbish out.

Things you should check include looking to see

that all the data is included in your project,

including any unmineralized holes,

understanding the impact that missing

and null values may have on your model,

and how the replacement rules are being implemented.

Are there any extreme outliers, highs or lows,

in your dataset?

Does the data look to be in the correct spatial location

relative to things such as development and topography?

And are there any duplicate points or overlapping intervals?

Leapfrog Geo automatically identifies and flags

common data errors when data tables are imported.

Until errors are corrected,

the rows that contain those errors are excluded.

For example, if the ID for a color

contains an invalid coordinate,

that color will not be displayed in the scene

as its location is not known.

This will also affect any downstream processing

and it will ignore any data associated with this drillhole.

We will now move into the software

to look at this in more detail.

This session will not cover building domains from scratch.

So throughout the demonstration,

I am going to make use of a project already created

using the saved scenes to show the functionality.

I’m going to use a dataset from a real underground goldmine

that has since been mined out.

Data locations and names have been changed

to keep the project anonymous.

Although I am demonstrating this functionality

with respect to our underground narrow vein gold system,

these validation tools can be useful

in almost any deposit style or geological setting.

If you need help with your individual project,

please don’t hesitate to contact

our friendly support team to assist you.

I will begin with validating the drillhole database.

Over in my project tree,

you can see I have a diamond drillhole database here

and I’ve imported all these various table types

to build my database.

The red exclamation marks here on my assay, geology,

and vein codes indicate that there is possibly an error.

The orange exclamation mark on my survey

indicates there is a warning.

The blue downward pointing arrow

indicates that the data has been imported correctly

and there is no errors to fix.

To fix an error, I right-click on one of these tables

that has an error and select fix errors.

This will open up a new tab

and it will show me where these errors are occurring.

This assay table actually has no errors,

but it has some invalid values handling.

If we look at this AU_PPM one,

we can see that some rules have been applied

to the non-positive values.

So by default, it is going to replace them with 0.01.

However, other rules have been applied

for other values with the general rule of thumb

usually replacing them with half the detection limit.

Leapfrog Geo identifies a range of common errors

and all errors identified are grouped by type.

You can click on any error

and expand it to find out more information.

Some errors will involve you

going back to the original database and fixing them

and some errors can be ignored within Leapfrog Geo.

Once the errors in the database are fixed and validated,

you can be sure your model will have the most up-to-date

accurate data feeding into it.

Next, I can check that my drillhole data

that I have imported is in the correct location

to some of my features such as my topography

and development drives.

I can rotate in the 3D scene

to check for any error in the survey data

and ensure my drillholes are where I expect them to be.

Using the slicer tool,

I can then look at validating my model

on a section by section basis.

Here, I’m looking down on a Z section at the 852 RL.

This shows that my mineralization model

is following these development drives.

If I step through a few more sections

using the greater than or less than keys on my keyboard

on a set step width,

I can see the model changes at other RLs

and how this matches my development.

Alternatively, I can do this process

on a vertical cross-section.

Here, I am looking north through a slice of my model

with development drives and drillholes.

If I stepped through my model using the greater than

or less than keys on my keyboard,

you can visually validate that your model

is matching those development drives

and following any drillhole logging or assays.

This method is one of the more traditional ways

to validate a model and it can be quite time-consuming.

Leapfrog also has some other unique tools

that help you identify areas of your model

that may require further investigation.

The first tool I will show you

is the correlation statistical tool.

This shows you the correlation between the model geology

and the log geology.

To do this, you need to evaluate your mineralized model

and your geological model onto your drillholes.

You do this by right-clicking on your drillhole database

and selecting new evaluation table.

I have already completed this process

and if I bring this evaluation of my mineralized domains

into my scene and have a look,

you can see it shows me

where my mineralized domains are on my drillholes.

Once I have a evaluation table set up,

I then can come to their evaluation in my project tree,

right-click and select statistics.

This will bring up a correlation statistics table

showing me my drilling lithology, my drilling length,

my matching length, matching percent,

non-matching length, and non-matching percent of my model.

In this example,

I’ve shown this for the mineralized vein system,

but this can also be used to test an entire geological model

to determine where more refinement is needed.

Alternatively, you can create a merged table

of your evaluated domains with your original assay data,

and then look at the statistics

to help validate your domains.

You do this up here in the drillhole database,

right-click on your drillholes and select new merged table.

Again, I’ve already created this

where I’ve merged my vein lithologies for my domain

with my gold grades.

I think can right-click on this table and select statistics.

You then can choose which type of statistics

you want to look at.

Firstly, I’ll look at this table of statistics

and this will open up a new tab showing you your domains

with the mean grade, standard deviation, variance,

and quantile data for each of your domain codes.

You can also do this for a box plot.

Again, right-click on your merged table, select statistics,

choose the box plot option

where you can view each of your domains as a box plot

to get a visual indication of where any outliers are

within your domains

and the main grades of each of these domains.

I’ve applied a log scale to this chart

to help see this better

as the status here is very negatively skewed.

I can also have a look at the histogram of a single domain.

This will allow me to examine the data distribution

within the domain and check it’s forming a population

that meets the assumptions of stationarity.

This is where the mean and variance

do not change across the domain

and will enable it to be used in downstream estimation

or numeric modeling.

To do this, I come to the merged table, open it up,

and come to the numeric data of interest,

right-click and select statistics.

This will open up a new tab

showing us the histogram of the data.

Here, I can apply a query filter.

I’ve built one just for this 4,400 domain

so I’m only looking at the data within that domain.

I can then detach this

and have a look at where some of the anomalous grades

are occurring in my scene view.

I will hide some of these estimation domains

and demonstrate how you do this.

So if I feel anything above this area was anomalous,

I could highlight and then I could have a look in my scene

to see if that is actually sporadic

or is it forming another domain population.

In relation to this 4,400,

if I turn that on and turn down the translucency,

I can see that this is pretty sporadic across my domain.

I can reattach that tab

and then I can also look at the histogram of the log

or the cumulative histogram.

Once you have a merged table

of your evaluated domains with grade,

you can then utilize query filters

to test things in your model.

Here in my scene, I have applied a query filter

to show me where there are bearing grades

included within my mineralized model.

I’ve created this query filter

by right-clicking on my merged table

and selecting new query filter.

I’ve previously built this so open this one up for you

and you can see the syntax applied to build that query.

We also have the easy build query function

where you can come in and apply your query.

Here, I’ve selected where my AU_PPM

is less than or equal to 0.3

and my estimation domains

are within these four domains shown.

We also have the advanced query filter builder

that you can utilize for your project.

Once I’ve built my query filter,

I can drag and drop that in the scene

and that query will be applied.

You can also apply the query filter

over here in your properties.

If I zoom in and look around my project,

I can now see areas that I need to go and investigate

to understand why these barren grades have been included

within my mineralized model.

Next, I can use a similar process

to test where I have mineralized grade

that has not been included in my mineralized model.

Here in the scene,

you can see I have applied this query filter

and I’ve built it by right-clicking again,

selecting new query filter.

And if I come into my prebuilt query filter, open that up,

you can see I’ve done the reverse of our first one

where I’ve said if it’s greater than or equal to 0.3

and it’s not in our domains,

then it’s considered mineralized outside of the domains.

This is then being applied in my scene.

And in this example,

you can see there’s quite a lot outside my model.

That is because there is actually quite a few

unmodeled domains in this project.

You can also use query filters to test areas of your model

to see where geology code logged

does not match geology modeled.

To do this, I’ve created an evaluation of my host rock model

and merged that with my geological lithology table.

I’ve then built a query filter here

to test where my end site is outside my model.

If I have a look at my scene,

I can rotate and zoom in on some of these areas,

click on a hole and I can go and investigate

why that is outside that geology unit.

If I did want to examine a hole in more detail,

I can use the drillhole correlation tool

here under my drillhole database

to examine in further detail.

I right-click on this and select new drillhole set,

which brings me out to a new drillhole set tab.

I then select the pit color function,

which will take me back to my scene view.

I then choose my select drillhole button

and choose the hole I’m interested.

I then can click save

and then it would appear here in my drillhole set layer

where I can bring across my host rock


and my original logging.

And then I can come in and identify

why I have these different areas

within my model versus my logging.

The final validation step for any project

is always to have your project peer reviewed

by one of your colleagues.

To do this, we are going to use Seequent Central.

I will show you this briefly.

However, we have another webinar on this

that was completed earlier this year

by one of my colleagues,

so please feel free to check out this video recording

on the Seequent website.

To upload your project,

you will need to be connected to the Central server.

You can do so by connecting up here

in your right-hand corner.

Once you are connected, you select publish.

You select which objects you would like to appear

in your online web browser.

Select next.

Set your project workflow stage.

I’m going to choose peer review for this one.

Select next.

Add in any revision notes for your colleagues

and then select next to start the publish and upload.

Once your project has finished publishing,

it will say publish successful here in the dropdown menu,

and it will be available for your colleagues to download

if they have the right permissions and access to Central.

It is also now available via the web browser

for non-Leapfrog Geo users such as managers

to review via Web-Verse and make comments.

To do this, you can select go to portal

and this will take you to the appropriate web browser

where your project is stored.

As you can see, it’s got the revision note

saying please review this project.

It’s got the stage of peer review

and it’s got the user who uploaded that project.

You can then select the project

to view within the web browser,

come to your geological model,

open up your domains and have a look at these

without needing access to a Leapfrog Geo license.

Comments can then be made by clicking on this comment button

and selecting add comments.

These can be geo tagged on your domain

and you can tag your colleagues here

so they get an instant notification

on any area to go and look at in further detail.

That brings me to the conclusion of today’s webinar.

If you require any further information,

please refer to the Seequent website,

which has many blogs, webinar recordings, and videos

to help you with your learning.

We also have a great series of free online courses

to help up-skill which can be found

under your MySeequent account.

If you do need any additional help,

please reach out to your local support team

who will be willing to assist.

Thank you.