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Lyceum 2021 | Together Towards Tomorrow

This presentation is a case study about a significant structural and lithological data inventory from Lumwana Mine’s Chimiwungo Deposit

Which was carefully interpreted in Leapfrog Geo to create a series of block-faulted ductile, tectono-stratigraphic domains and internal structural form interpolants that encompass an intensely strained 100-meter-wide shear zone. Discover how dynamic modelling workflows and the resultant 3D model honours structural cross-cutting relationships and depicts deposit- to regional-scale kinematics, and learn how the model is currently being used for forward geotechnical domaining.



Corné Koegelenberg
Principal Geoscientist, Tect Geological Consulting


20 min

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Video transcript

(techno music)

Hello everybody, thank you very much for tuning

into the Lyceum and giving us the opportunity

to present some of the work we’d like to present.

What I’m presenting today is a project

that we’ve started in 2016.

It’s run up to 2019, it largely revolves

around 3D modeling of the Lumwana mine,

in Zambia.

Now it showcases some of the really distinct capabilities

of Leapfrog Geo and largely how you can sort of

feed a conceptual structural study into the modeling sphere.

So it’s essentially what we call a structural model,

is when you can sort of see

the conceptual structural understanding in a model.

And this is one of the projects that we like,

to show people just to showcase what Leapfrog is all about.

And so to speak, what a structural modeling

is all about.

So I was leading the team on the ground at the mine.

We also had Hastings Lupapulo, we had Vincent

from the geotechnical department

with Peter Hornsby as well, assisting on the ground.

These guys largely work for geo-tech

and sort of structural departments.

And obviously those other guys are most interested

in the products that we ended up

delivering at the end of the day.

So to get into it.

Firstly, is the conceptual sort

of structural evolution of the mine.

Lumwana sits in the Lufilian Arc.

The Lufilian Arc’s also well-known

for basically hosting

the DRC copper belt.

And the Zambian copper belt.

It’s part of one big great sort of collisional Arc.

Now what makes Lumwana mine unique is

that it sits in the core of this Arc.

It’s not as a typical sort of,

external fold and thrust belt setting.

Unlike for most of the largest copper mines,

copper cobalt mines in the world.

Lumwana is actually sitting in the core

where it’s situated on,

deepest structural terrain.

You’re looking at high metamorphic degrees

and really very, very high strain rates.

So if you look at the location,

the mine situated on the Mwombezi dome.

It’s one of the windows into the basement,

essentially a window through the Katangan Supergroup,

which has largely defined your Lufilian Arc.

In that the mine

exposes shear zones

in between the Katangan Supergroup and the basement.

Whereas the mine is actually

sensu stricto acidic in the basement.

The basement around this particular deposit

consists of large sort of thrust sheets.

If you can imagine it’s like a deck of cards

that’s just sort of slided up to the one side.

That’s essentially what the tectonics

or the tectonostratigraphy this mine looks like.

So it’s a very unique setting in the copper belt region.


To just show you where the Chimiwungo Deposit lies.

The Chimiwungo Deposit forms part of the Lumwana mine.

It’s actually one deposit, the other deposit is,


That is to the east whereas Chimiwungo is to the South.

the general stratigraphy of the mine,

is what we call a tectonostratigraphy.

It’s not a typical sort of sedimentary stratigraphy.

You’re looking at sort of sharp thrust sheets

lying on top of each other.

The base of this stress stratigraphy,

is what we typically refer to as gneiss

or gray gneiss package.

That’s very competent gneiss or undifferentiated gneisses

that line with schists or interlayed with schists.

Now, it’s not just a flat sort of package.

There are folded gneisses or isoclinal folded gneisses.

They are in between the isocline folds you get

on the limbs that are bound with shear zones.

And you can also see a gneiss fold

on one of the cores there.

If you go on top of that unit, you’re going from a footwall.

And then you’re going into what’s typically

referred to as an ore schist.

Now an ore schist is what’s essentially,

the thrust.

That is in between some of these larger thrust sheets.

So the ore schist is in the shear zone.

This is a ductile setting.

So the ore schist is essentially,

the equivalent of a fault gouge

or a sort of a fault breccia in a brittle setting.

It’s a complete rock unit that was created

with dynamic metamorphisms.

Basically as you ingress fluids, you change the composition

and the structure of the rock.

As you can see, it is intensely foliated.

And largely the mineralization

is sitting on top of the folia.

The mineralization is largely chalcopyrite

or copper-bearing sulfides.

Depending on the level of oxidation

that’s more copper, less sulfur, less oxygen.

What you can see in the top right there as well,

is that this unit is extremely thick in some cases.

The ore schist can be up to 100 meters thick in some parts.

Shear zone that’s 100 meters thick.

It’s a pretty significant site,

and you can also see the footwall exposed there.

Our ore schist is sort of copper, like a bronze color.

That’s largely the oxidation of the sulfides on the surface.

It’s a really spectacular deposit.

If you ever a chance to go there, please visit.

Now on top of that, if you’re going into the hanging wall,

it’s the top most layer of the tectonostratigraphy.

You can see that the contact sometimes is sharp.

And it’s all not always very sharp.

You can see a slight transposition

going from the ore schist into the gneisses on top.

So you can see how the gneiss is progressively

being infected by the dynamic metamorphism,

creating more and more ore schists.

It’s typically also what we refer to,

as sort of strain-softening.

So as you change the composition and the rock type,

it starts to accomodate more strain.

That’s largely because you’re going from competent gneiss

into ore schists that’s very, very strained.

Now the top unit, which is the pink gneiss.

That’s the thrust or the layer right on the top.

It’s extremely competent like the footwall.

In this case, it’s just a gneiss with a different composition,

a bit more potassium in the system.

So it sort of just gives you that beautiful pink color.

Now again, because of the pink gneiss competency,

it can’t absorb the same amount of strain

as you see in the shears on it.

So what happens with the spent gneisses

to accommodate sort of a flattening strain

across the entire deposit.

It’s got to break somewhere.

It’s got to sort of accommodate strain.

And what it does is that you form

very narrow shears of it in this package.

And you can see that these shears are obviously shifting

or allowing this package to accommodate

or to fit into the sort of narrow stratigraphy.

Now these shears are due technically very, very bad

or very significant for mining.

In that you don’t want them to be exposed

in the unfavorable sodum geometry geo pit

because they are prone to be very weak.

So if you take the evolution

or the structural evolution of the sites.

All the elements into consideration,

you can see the three distinct

sort of stratographical packages.

And you can see that the strain across the entire mine,

in this case across those three stratographical packages

is accommodated differently.

So the footwall is different to the shear zone,

is different to the hanging wall.

And all the internal sort of structures are rotated.

and sort of align is different.

So you’ve got to treat every sort of single package,

especially if you’re modeling them,

you’ve got to take all these things into consideration.

If I go towards the southern end of the deposit

across the Southern pit,

you can see the same sort of features.

The important bit here is that for the mining perspective,

is that the shear zone, and the ore schists,

and obviously the hanging wall

is largely exposed in the pit.

And very little of the footwall is.

Obviously in mining past the ore schist,

you don’t want to be mining too much waste.

So if you can see a long section through the mine,

apart from it being a ductile setting,

you can also see that there are

significant sort of brittle faults.

Now these faults are much, much, much younger

than the sort of structural evolution.

All of the events that actually form the deposit.

These faults sort of cuts from east to west across the mine.

And they obviously segment the deposits

into various fault blocks.

And those are also very significant

for the modeling considerations.

Again just a nice summary of everything we’ve seen.

Just a bit more 3D is that you can see,

sort of the different kinematics

stemming across the ore schist into the hanging wall

And all these things we’ve tried to conceptually figure out.

We’ve got a very good understanding of the model

and structure that formed the deposit.

Now the name of the game is,

how do we model all these elements?

It was quite a sort of careful exercise,

in that we’ve got a very good conceptual

understanding of the model.

What’s significant, is that we had a extremely

sort of large database of structural data.

We were able to map all the pits extensively.

we had 1200 hundreds of stations.

There were more than 3000 measurements.

Individual measurements of fabrics,

and foliations or any relevant features.

The structural, from a structural logging point of view.

Exploration logs provided us with more than,

12,000 measurements, conventional readings.

So you know, actual geologists log those readings.

it’s good readings.

And if you’ve got 12,000 readings it’s really grateful for,

I’m sort of comparing data sets.

Again we also had some ATV

and OTV micro structural readings.

Now the most important part of all these datasets

for us was to do a bit of due diligence.

And discretization of the data.

So the first, due diligence is obviously

where we checked if all the data sets match.

Because with massive data sets like this,

each sort of unique data set’s got a bit of a bias to it.

We had to make sure that we sort of,

did not give any particular dataset,

more weight because of a certain bias.

We had to sort of straighten out the data.

As well as discretization is for each sort of block

or each domain that we looked at,

to make sure that the populations overlapped.

Now, we did this essentially for foliations

across the entire body.

We looked at

joints across the entire body,

and you can see it’s sorting various datasets.

Lots and lots of sort of, staring at analysis.

And also we looked at faults and making sure

that we weigh the data properly.

And obviously beyond this,

we had to start looking at structural domaining.

How are we going to domain or segment this model?

What we did was firstly, and this is a logical step,

was to use some of the significant brittle faults

to define brittle fault block domains.

Now you’ll see that there are some of these brittle faults

that we have not selected.

They obviously represent very little offsets,

maybe two to three meters up to six meters.

Some of the larger faults are far greater.

I mean the orange faults which define the blocks taken,

can throw the whole body off by 20 30, 50 meters.

That was a really significant fall for geotechnical

and sort of, resource modeling purposes.

Beyond that, obviously we spoke about the tectostratigraphy,

is that we went on to divide the model.

The model volume then between hanging wall,

ore shifts and footwall.

To being cognizant of all the different

sort of structural features in each one of those blocks.

Now what’s nice about this, you could really see

if you just take out the ore schist.

And you look at the topography which is dip shaded

and ore schist sort of thickness.

You can already start to see sort of structural elements

coming out of the shear zone,

that sort of matches conceptual sort of structural model

that we initially show.

Now in this case you can see very sort of small,

secondary peracetic folds.

as well as sort of larger gentle scale folds.

In the model later on you’ll see,

how these sort of structurally, come into play.

So what we did was to,

sort of use these

tectonostratographic domains.

We wanted to model inside of them, we wanted to refine them.

But how do you define a particular structural feature

or particular dominant structural trend

in each one of these domains.

Obviously as to use the

large structural database that we had.

And in this specific case, we sort of divided the data

into each one of the domains, and then we groomed the data.

Now here strictly when I’m referring to grooming,

is when we take the careful due diligence

and structural discretization that we did was,

to remove outliers in a spatial way.

Leapfrog is really, really good for doing this.

In that you can use the Leapfrog stereonet functionality.

Whilst you’re looking at stereonet, you can also spatially

assess the data and where it’s sitting.

So it’s really, really great to remove outliers

and not just on a geometrical base

but on a spatial sort of base as well.

We sort of groomed through all of the data

to make sure that it fits the conceptual structural

understanding of each domain.

And just as a caution, I say grooming and not de-clustering.

So in any sort of structural model and with this database

is that you want to make very, very,

carefully sure that you feed data into Leapfrog that is,

how can I say, already checked and discretized.

Because the structural, the de-clustering tool which is

automatically used in some of these modeling processes,

basically removes duplicates.

So you don’t want, in a certain sense,

when you’ve got dominant groups.

the dominant groups will be weakened

because you’re removing duplicates.

And those obviously that are not duplicates.

The sets that do not form part of the main groupings

will be more precise or more dominant.

So it’s very important to do your sort of grooming

before you use these tools.

Not afterwards or whilst you are using them.

You need to sort of do it in advance.

Now as you can see, what’s really nice is

when we built form interpolants it’s a really nice tool.

And Leapfrog is essentially showing you,

general structural trends through

sort of a point cloud of structural data.

And you can see for each of the tectonostratigraphy,

of one of the domains.

The form interpolants in this case showing you

in ore schists, the Schistocity of the ore schists.

In the top, the footwall and the hanging wall,

more of a Gneissocity.

And you could see the kinematics sort of rotation

of these form interpolants.

Fitting the conceptual structural model very nicely.

We were very happy with these results.

So with that sets, if you zoom out,

we had enough data to construct this

across the entire sort of mining volume to acertain.

Obviously we’ve got more data, you’ve got more accurate

sort of structural form interpolants.

More further away from the data you have to use other

sort of sets, or interpolations, or interpretations.

For example, just using the contacts.

But largely this sort of model was informed

by the large structured data set that we had.

Now that we’ve established the structural trend

for each one of these domains and on a block by block basis.

We could continue to do a refined lithological model.

A lithological modeling that essentially came down

to doing sort of a more pure implicit modeling,

Where we fit the form interpolants.

Obviously trying to keep the volume that we were modeling

for a particular block, as small as possible.

Allowing Leapfrog to work at a greater resolution.

Okay, so now that we’ve defined our sort of structural brain

in our tectonostratigraphic domains.

We from a more pure implicit way try

to model the detailed lithology.

So obviously in your hanging wall

and your footwall sort of domains,

we extracted gneissositian schists.

Obviously undifferentiated gneissositian schists.

Whereas for the ore schists, we looked at essentially

everything that is considered ore and not considered ore.

Those that’s not considered ore is typically waste lenses.

Now these waste lenses are bodies of ore chunks,

of hanging or footwall floating around in the ore schists.

Only those things you want to sort of pull out

or not feed into your crusher when you are mining.

So if you put a slide through this,

you can really see how the implicit modeling

and the structural trends work together

and dragging out the geometry of the shear zone.

Obviously, the hanging wall and the footwall as well.

Like I said, we were really pleased with these results.

If you consider that the massive drill hole database,

that this is an instructional data.

All this sort of working together giving you a really,

sort of concise structurally accurate model.

What we did beyond that is to even go further.

So in serving a refined model what we did was

to extract waste lenses, like I’ve just mentioned.

On a higher resolution.

So what we did was to evaluate volume metrically

structural data and also form interpolants inside

and outside waste lenses but still within the ore schist.

And we used those two to build

even higher resolution structural trends.

And what you can see is that from that,

we sort of redid the pure, sort of implicit modeling.

And what we got was really, really accurate

sort of geometries that really sort of represent

what a natural sort of shear zone looks like.

And again, we were pleased, really pleased with the results.

So beyond that because it’s a structural model

and because we’ve largely worked not only with resource,

but mostly with geotech,

is to use the ore schist

to define a geotechnical or structural geotechnical domains


we divided the ore body as well

as the hanging wall up into sections.

Sort of different sections for kinematic analysis

across the deposit.

We looked at sort of ore thickness, ore schist thickness.

But more importantly, we looked at some of those peracitic

and more gentle folds to sort of establish domains

where you get a more consistent geometry font

from particular features.

And the model really, really sort of aided

that whole thought process.

Beyond that we’ve also taken the form interpolants

or the dominant fabric orientations

in each one of those domains.

And we’ve juxtaposed it against the sort of,

long-term design or pit designs.

And in this case we can see red, warmer colors

versus colder colors.

Obviously red in this case would mean

more unfavorable situations.

Whereas the blue would mean that it’s really favorable.

In this case,

what we categorized favorable and unfavorable

was the fact that you had foliation that is day-lighting.

So if the dominant foliation is day-lighting outside

of your pit, you would obviously have some

of those really bad

shears that you saw on previous slides day-lighting.

You don’t really want that.

So the red zones largely represent areas

where you could possibly end up having that scenario.

Whereas the blue area, you end up not having that scenario.

Now this is a sort of a double-edged sword

in that you can mitigate some of these risks.

But you can also optimize on either end

where the slopes are blue.

You try and perhaps increase the slope if you want to.

These are all sorts of factors,

that feed into the whole geotechnical design process

for this particular mine.

So yeah those are some of the elements

that we were able to draw out from the structural model.

Like I said, a structural model

starts with a conceptual understanding of your deposit.

And then that feeds into Leapfrog,

which absolutely allows you

to sort of realize that sort of structural concepts in 3D.

And that obviously feeds into downstream geotechnical

and resource capabilities that are very, very accurate.

To satisfy geologists like myself,

who would like to see structural

or complex structural geometries being realized.

Yeah, thank you very much.

Thank you for your time.

And I hope you enjoy the rest of the conference.

(techno music)1