Mark Jessell - Next Generation 3D Modelling

Mark JessellWA Fellow & Winthrop Professor

Laurent Aillères, Tom Carmichael, Monash UniEric de Kemp, Mike Hillier, Geol. Survey CanadaRoland Martin, CNRS, ToulouseMark Lindsay CET UWAStéphane Perrouty, Uni Toulouse

Next Generation 3D Modelling & Inversion

After Dante 1315

Aims of WA Fellowship Program

• Better integrate geological constraintsinto 3D regional modelling

• Carry geological meaning through the geophysical inversion process

3D Model(s) of AustraliaGautier Laurent et al 2013

ARC DP1096409 Betts, Aillères, Jessell & de Kemp

Active MinesExisting or planned 3D models

A good question

Sedimentary Basins

Mines Regional Lithosphere

3D Constraints

RICH (3D seismic, deep boreholes, gravity)

RICH (dense boreholes,magnetics, seismic, electromagnetics)

POOR (rare boreholes, surface outcrops, gravity, magnetics)

RICH(Teleseismic, seismic, gravity, MT)

Structural Complexity

SIMPLE(R) COMPLEX COMPLEX SIMPLE(R)

Dedicated Software

Gocad 1989, Geomodeller 1999…

MicroMine 1986,Leapfrog 2003...

Noddy 1981 Gocad 1989

3D geomodelling scenarios

Jessell, 1981

Knowledge Data Data + Knowledge Data + Knowledge + Uncertainty

Short history of 3D modelling

3D geology is an under-constrained problem

We do not have sufficient geological and/or geophysical data to define a unique 3D model

We should not restrict ourselves to a single 3D model

3D Modelling tools that require continualmanual intervention are a dead end

Ashanti Belt

3.6 - 2.5 Ga

2.3 - 2.0 Ga

after Milési et al., 2004, BRGM SIGAfrique

Gold deposits

Orogenic

Placer

______10 km

Perrouty et al., submittedEcon Geol 2013

West African Craton

3D Prospectivity Analysis

Tarkwa Basin

Kumasi Basin Akyem

BasinVolume : 160*160*15 kmResolution : 200 m

Model (after inversion)

0 (m)

-14000

Depth(Faults)



BVC2BV1

BVC1

xy

- Measure the minimum distance between deposits andthe different Sefwi Group units

- Count the number of deposits found less than 1500 mfrom each unit

BVC2 BV1 BVC1

Distance 0 m x y

Fault or unconformity


Number of deposits less than 1500m from each unit

Stra

tigra

phic

Dep

th(m

)


Challenges

Better Inputs

Better use of geology duringmodelling & inversion

Better analysis of results of modelling

a) Trainingb) Structural analysisc) Geophysical imaging

a) Training:Structural Geophysics Course

κ’ Kent Distribution(measure of clustering)

b) Structural analysis:Intelligent upscaling

Tom Carmichael, Monash Uni

Limousin, France

c) Geophysical Imaging: Full Tensor Gravity

Daniel Wedge, CET (Data from First Quantum) →ARC Linkage

Challenges

Better Inputs



a) Better use of field datab) Geologically Appropriate interpolation schemesc) Integrated Inversion

a) Better use of field data Alsop et al., 1996(Near Moine Thrust)

Structural Interpolation

Geology 101

?

?GeomodellerSKUALeapfrog

Jessell, Aillères, de Kemp & 1000Structural geologists

a

b

bedding

d

c

?

Geology 201

Bedding Only

a

b

d

c

F1F2

Need vergence of minorfolds or Sn/Sn+1 to constrain fold model

F1

bedding

S2 cleavage

Geology 201

Bedding-CleavageRelationships with Relative Timing

S1 cleavage

Multiquadric RBF (stratigraphy, faults?)

Michael Hillier (Geol Survey Canada)

Same input data:

Geologically Appropriate

interpolation schemes

Geology 501

RBF=Radial Basis Function



Gaussian RBF (salt domes?)


Same input data:

Geology 501


Thin plate spline RBF (folds?)


Same input data:



Geology 501


Doesn’t explaingeophysical signal

c) Integrated Inversion • Structures• Age Relationships• Petrology• Geophysics• Petrophysics• Prior Knowledge•••

Petrophysics

• Need inversion schemes thatretain geological meaningthrough the inversion process

• So we can test the resultsagainst the original geologicalAND geophysical data

• Currently working on speedingup inversion so that in the future we can include bettergeological constraints

Challenges

Better Inputs



a) Uncertaintyb) Geodiversity

Vary inputs:• Orientations• Position• Age

relationships

Original Inputs

Perturbed Inputs 1

Perturbed Inputs 2

Perturbed Inputs 3

Perturbed Inputs 4

Perturbed Inputs N

•••

Implicit Modelling

Engine

Wellman et al., 2010, 1011Jessell et al., 2010Lindsay et al., 2012,2013

a) Uncertainty & Simulation

Stratigraphic Variability = number of possible lithologies

Lindsay et al., 2012

• Where do we need to collect more data?

• Use variability to weight petrophysical inversions

1 Lithologies per voxel 6

0

0

0.40.30.2

0.4

-0.1-0.2-0.3-0.4

-0.3

-0.4

-0.2

-0.1

0.1

0.3

0.2

0.1

0.5

-0.5

0.5-0.5

b) Geodiversity via Principal component analysis

Original Model: 11th closest to

barycentre

Studying variation between plausible models

• Initial model NOT the most representative model

• Use diverse range of models to seed geophysical inversions

Gippsland Basin model suite

Lindsay et al., 2013

Based on multiple geologicalattributes: unit volume, unit depth, surface complexity, geophysical misfit…

Principal Component 1

Prin

cipa

l Com

pone

nt 2

Better Inputsa) Trainingb) Structural analysisc) Geophysical imaging

Better use of geology during modelling & inversiona) Better use of field datab) Geologically Appropriate interpolation schemesc) Integrated Inversion

Better analysis of results of modellinga) Uncertaintyb) Geodiversity

3D Modelling Challenges

ARC Linkage Submitted: Reducing 3D uncertainty via improved data interpretation methodsMark Jessell, Eun-Jung Holden, Mark Lindsay, Klaus Gessner, Jon Hronsky

ARC Centre of Excellence Submitted: Computational Geoscience and Earth ModellingMultiscale Analysis & Modelling

Western Australian Fellowship: WA_In3DMark Jessell

The problem that emerges when a model of a phenomenon is just as hard to understand as the phenomenon that it is supposed to explain.

Everything simple is false, everything complex is unusable

Paul Valéry, 1937

Bonini’s Paradox

The challenge we have set ourselves is to make our models (and modelling systems) less false, without becoming unuseable

Self Improvement

Mark Jessell - Next Generation 3D Modelling