Published on March 12, 2014
Mark Jessell WA Fellow & Winthrop Professor Laurent Aillères, Tom Carmichael, Monash Uni Eric de Kemp, Mike Hillier, Geol. Survey Canada Roland Martin, CNRS, Toulouse Mark Lindsay CET UWA Stéphane Perrouty, Uni Toulouse Next Generation 3D Modelling & Inversion After Dante 1315
Aims of WA Fellowship Program • Better integrate geological constraints into 3D regional modelling • Carry geological meaning through the geophysical inversion process
3D Model(s) of Australia Gautier Laurent et al 2013 ARC DP1096409 Betts, Aillères, Jessell & de Kemp Active Mines Existing 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 continual manual 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., submitted Econ Geol 2013 West African Craton 3D Prospectivity Analysis
Tarkwa Basin Kumasi Basin Akyem BasinVolume : 160*160*15 km Resolution : 200 m Model (after inversion) 0 (m) -14000 Depth (Faults) 3D Prospectivity Analysis Perrouty et al., submitted Econ Geol 2013
BVC2 BV1 BVC1 xy - Measure the minimum distance between deposits and the different Sefwi Group units - Count the number of deposits found less than 1500 m from each unit BVC2 BV1 BVC1 Distance 0 m x y Fault or unconformity 3D Prospectivity Analysis Number of deposits less than 1500m from each unit StratigraphicDepth(m) Perrouty et al., submitted Econ Geol 2013
Challenges Better Inputs Better use of geology during modelling & inversion Better analysis of results of modelling a) Training b) Structural analysis c) 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 Better use of geology during modelling & inversion Better analysis of results of modelling a) Better use of field data b) Geologically Appropriate interpolation schemes c) Integrated Inversion
a) Better use of field data Alsop et al., 1996 (Near Moine Thrust)
Structural Interpolation Geology 101 ? ? Geomodeller SKUA Leapfrog Jessell, Aillères, de Kemp & 1000 Structural geologists
a b bedding d c ? Geology 201 Bedding Only
a b d c F1 F2 Need vergence of minor folds or Sn/Sn+1 to constrain fold model F1 bedding S2 cleavage Geology 201 Bedding-Cleavage Relationships 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
Geologically Appropriate interpolation schemes Gaussian RBF (salt domes?) Michael Hillier (Geol Survey Canada) Same input data: Geology 501 RBF=Radial Basis Function
Thin plate spline RBF (folds?) Michael Hillier (Geol Survey Canada) Same input data: Geologically Appropriate interpolation schemes Geology 501 RBF=Radial Basis Function
Doesn’t explain geophysical signal c) Integrated Inversion • Structures • Age Relationships • Petrology • Geophysics • Petrophysics • Prior Knowledge • • • Petrophysics • Need inversion schemes that retain geological meaning through the inversion process • So we can test the results against the original geological AND geophysical data • Currently working on speeding up inversion so that in the future we can include better geological constraints
Challenges Better Inputs Better use of geology during modelling & inversion Better analysis of results of modelling a) Uncertainty b) 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, 1011 Jessell et al., 2010 Lindsay 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 geological attributes: unit volume, unit depth, surface complexity, geophysical misfit… Principal Component 1 PrincipalComponent2
Better Inputs a) Training b) Structural analysis c) Geophysical imaging Better use of geology during modelling & inversion a) Better use of field data b) Geologically Appropriate interpolation schemes c) Integrated Inversion Better analysis of results of modelling a) Uncertainty b) Geodiversity 3D Modelling Challenges
ARC Linkage Submitted: Reducing 3D uncertainty via improved data interpretation methods Mark Jessell, Eun-Jung Holden, Mark Lindsay, Klaus Gessner, Jon Hronsky ARC Centre of Excellence Submitted: Computational Geoscience and Earth Modelling Multiscale Analysis & Modelling Western Australian Fellowship: WA_In3D Mark 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
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Mark Jessell CET UWA Laurent Aillères (Monash Uni) Eric de Kemp (Geol. Survey Canada) Roland Martin (CNRS Toulouse) Mark Lindsay, Florian Wellmann (CET UWA)
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