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.
Principal Geoscientist, Tect Geological Consulting
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,
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,
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
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.