Using potential field data and stochastic optimisation to refine 3D geological models

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“Using potential field data and stochastic optimisation to refine 3D geological models” by Richard Lane (Geoscience Australia, richard.lane@ga.gov.au), Phil McInerney (Intrepid Geophysics, …

“Using potential field data and stochastic optimisation to refine 3D geological models” by Richard Lane (Geoscience Australia, richard.lane@ga.gov.au), Phil McInerney (Intrepid Geophysics, phil@intrepid-geophysics.com), Ray Seikel (Intrepid Geophysics, ray@intrepid-geophysics.com), and Antonio Guillen (BRGM and Intrepid Geophysics, a.guillen@brgm.fr). Paper presented at the Geophysics Session, PDAC, Tuesday, March 4 2008, Toronto, Canada. Abstract : As a geoscience agency, Geoscience Australia has sought a platform that allows us to integrate complimentary but diverse sources of information into consistent products. Several groups have made progress by blending 3D geological mapping and potential field modelling. We describe the approach implemented in GeoModeller software and illustrate typical workflows using a synthetic example and a case study involving the San Nicolas volcanogenic massive sulphide deposit. Starting with an initial 3D geological map, typically based on sparse surface observations, we utilise potential field data to investigate the viability of the proposed configuration of geological units at depth. Forward modelling of the property distribution derived from the 3D geological map and supplied rock property estimates allows us to simulate any of the gravity and magnetic fields or their associated vector or gradient tensor components. A visual comparison of the calculated and observed potential field data provides immediate feedback on the consistency between the 3D geological map and the observed potential field data. We may also use a bounded property optimisation procedure to derive an alternate combination of properties for the geological units (i.e., the combination that would best reproduce the supplied potential field observations). A review of the results obtained with these two simple procedures is used to identify any significant changes that are required for the 3D geological map or our estimates of the properties. Several iterations of geological mapping, forward modelling and property optimisation are generally required to derive a “reasonable” candidate 3D geological map for further consideration. At this point, a powerful geometry optimisation procedure can be used to fully invert the potential field data. The ambiguity that is inherent in this process is reduced by simultaneously inverting any number of gravity and magnetic data types and by doing so with strong geological constraints. The procedure utilises random elements and statistical decision rules to produce a large number of viable models, in contrast to the more common deterministic approach that results in a single “best” model. Statistical techniques are then used to analyse the acceptable models and identify important features of the 3D geological maps that are consistent with both geological and geophysical observations.

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  • 1. Using potential field data and stochastic optimisation to refine 3D geological models Richard Lane (Geoscience Australia) Phil McInerney and Ray Seikel (Intrepid Geophysics) Antonio Guillen (BRGM and Intrepid Geophysics) © 2004 BRGM & IntrepidPDAC 2008
  • 2. Outline of presentation• Demonstrate a set of tools for gravity and magnetic modelling• Used to refine 3D geological maps• Incorporating 3 complementary procedures – Forward modelling – Property optimisation – Geometry optimisation• Synthetic example (dipping slab)• Field example (San Nicolas VMS deposit)
  • 3. Workflow outlineGeological observations Mass density and magnetic Gravity and magnetic property observations observations 3D geological map (geometry) and bulk property estimates Supply geometry Supply geometry Supply properties and properties Forward model Optimise Optimise properties geometry Simulated Simulated Simulated response response response + + property revised geometry estimates Reject or revise the geological map and property estimates
  • 4. “Slab” synthetic example• Part 1 – Create a 3D geological map with a dipping slab unit in a host unit • e.g., VMS deposit in a host sequence – Assign anomalous density and magnetic properties to the slab – Generate synthetic data – Add random noise to form an “observed” data set• Part 2 – Try to recover the source feature
  • 5. Triangulated form Voxel form Density Susceptibility Remanent Magnetisation t/m3 SI x105 A/m Slab 3.27 1000 0.5 ( I = 35°, D = 135° ) Host 2.67 10 0Vertical gravity (gD) TMI ( I = -65°, D = 25° )
  • 6. Surface geology (i.e., Slab not visible!) Prior property estimates Density Susceptibility Remanent Magnetisation t/m3 SI x105 A/m Slab 3.27 1000 ??? Host 2.67 10 0 Vertical gravity (gD) + noise TMI ( I = -65°, D = 25° ) + noise
  • 7. Build an initial 3D geology map• We observe discrete gD and TMI anomalies• Propose a VMS deposit as the source – Elevated density and magnetisation Initial geological map in triangulated surface form relative to the host• Build a simple 3D geological map with a buried deposit (vertical slab geometry) in a host unit Discrete voxel form
  • 8. Density Susceptibility Remanent MagnetisationPrior property t/m3 SI x105 A/m estimates Slab 3.27 1000 ??? Host 2.67 10 0 Vertical gravity (gD) TMI ( I = -65°, D = 25° )Observed dataForward model data
  • 9. a1 * Response for Property geological unit #1 optimisation Response for + a2 * geological unit #2 … Response + an * for geological unit #n + an+1 * Trend = Observed response Optimise a1 to an+1 (which are the property contrasts)
  • 10. Density Total effective magnetisation Recovered t/m3 A/m property Slab 3.76 (3.27) 0.63, I=-15°, D=115° (0.39, I=-11°, D=111°) estimates Host 2.67 * ( Susceptibility 18 SI x105 )(true values in Vertical gravity (gD) TMI ( I = -65°, D = 25° ) braces)Observed data Property optimisation data (Assuming remanent magnetisation of unknown direction for Slab)
  • 11. Start Geometryoptimisation Load geological and property models Propose changes to the models Fail Apply geological tests Pass Fail Forward model and apply geophysical tests Pass (Modifications rejected, (Modifications accepted and saved)revert to previous models) Continue? Finish
  • 12. Boundary modification scheme• Select at random a voxel that is on a geological boundary• Propose a change to the geological assignment by randomly choosing from the list of map units in the neighbourhood of this voxel• Re-sample properties according to the distribution for the new map unit Present model• Then … – Apply geological tests – Calculate geophysical response – Apply geophysical tests• And repeat the process (over and over) Proposed model
  • 13. Vertical gravity (gD) Observed gD Calculated gD (final)Total Magnetic Intensity (TMI) Observed TMI Calculated TMI (final) I=-65°, D=25° I=-65°, D=25°
  • 14. Geological reference model Vertical Section750 m (‘most probable’ prior model) 500 mN 2 km Slab Host ‘True’ model ( Vertical exaggeration 1:1 )
  • 15. Animation of the geometry optimisation process for Section 500 mN( Vertical exaggeration 1:1, frames captured at increments of 500 proposals from 1 to 100,000 proposals )
  • 16. Geological reference model Vertical Section750 m (‘most probable’ prior model) 500 mN ‘Most probable’ (posterior composite model) 2 km ‘Most probable’ thresholded Slab (posterior composite model Host with P ≥ 95 % ) Probability shown with logarithmic scaling ‘Probability’ for Slab 100 % 10 % 1 % (or less) ‘True’ model ( Vertical exaggeration 1:1 )
  • 17. Comparison of geometry optimisation using single and multiple data typesVertical gravity (gD) TMI Joint gD and TMI Probability shown with logarithmic scaling Prior reference model 100 % 10 % True model 1 % (or less) ( Vertical section 500 mN, Vertical exaggeration 1:1 )
  • 18. San Nicolas VMS deposit, Mexico Vertical section -400 mN Mafic Volcanics Tertiary Breccia 2000 175m Elevation (m) Quartz Massive Sulphide Rhyolite Mafic VolcanicsBasin and Range el” “ Ke 1600 Province -2000 Easting (m) -1100 San Nicolas (Section from Phillips et al., 2001) Cenozoic cover Mesozoic basement (Section adapted from a figure supplied by the USGS)
  • 19. Interpreting gravity and magnetic dataas part of the exploration of the region• Purpose – To identify and perform preliminary evaluation of VMS targets• Data assessment – Recognition of the importance of the Cenozoic cover for generating the geophysical response• Determine the thickness and properties of the Cenozoic cover• Select targets• Assess the viability of the targets• Provide information to assist drill testing of the viable targets
  • 20. Surface topography Surface geology Rock properties Density Susc. Cond. t/m3 105 SI mS/m Cenozoic 2.17 10 – 400 40 Mesozoic 3.27 1000 50 (Sulphides) Mesozoic (Mafic?) 2.72 100 N/A Mesozoic 2.67 10 Low AEM conductance Vertical gravity RTP TMI 12 kmHot colours = high conductance (Adapted from material supplied by various sources)
  • 21. Cover / basement vertical gravity forward modelling and property optimisationThickness ofthe Cenozoic Observed gDcover derived from AEMconductance estimates -4.0 to +0.5 mGal0 to 250 mForward model Property gD optimisation gDCover 2.30 t/m3 Cover 2.18 t/m3Basement 2.67 t/m3 * Basement 2.67 t/m3 *-4.0 to +0.5 mGal -4.0 to +0.5 mGal
  • 22. Vertical gravity (gD) Cover / Basementgeometry optimisation Observed gD Calculated gD (for final model) Misfit gD (for final model)
  • 23. Section - 400 mN2 km Geological reference model (‘most probable’ prior model) 12 km Cenozoic cover Mesozoic basement ( Vertical exaggeration 1:1 )
  • 24. Animation of the geometry optimisation process for Section -400 mN( Vertical exaggeration 1:1, frames captured at increments of 20,000 proposals from 1 to 8 million proposals )
  • 25. Section - 400 mN2 km Geological reference model (‘most probable’ prior model) 12 km ‘Most probable’ posterior composite model Cenozoic cover Mesozoic basement ‘Most probable’ posterior composite model ( P ≥ 95 % ) Logarithmic scaling 100 % 10 % ‘Probability’ for Cenozoic cover 1 % (or less) ( Vertical exaggeration 1:1 )
  • 26. Section - 400 mN2 km Cenozoic cover Mesozoic basement ‘Most probable’ posterior composite model Zero thickness Thickness of Cenozoic cover ( i.e., basement outcrop or shallow sub-crop ) ( from geometry optimisation of basement/cover model ) Contour interval 100 m
  • 27. Initial selection of targetsVertical gravity residual
  • 28. Initial selection of targetsVertical gravity residual RTP TMI Region for detailed joint gD and TMI geometry optimisation ( 1200 m E/W and 800 m N/S, 25 m cell size )
  • 29. Geological reference model Section - 400 mN1000 m (‘most probable’ prior model) 1200 m ‘Most probable’ (posterior composite model) 12 km ‘Most probable’ thresholded Cenozoic cover (posterior composite model Mesozoic basement (sulphides) with P ≥ 95 % ) Mesozoic basement (mafic) Mesozoic basement Probability shown with logarithmic scaling ‘Probability’ for Mesozoic 100 % basement (sulphides) 10 % 1% (or less) Supplied drill section (simplified) ( Vertical exaggeration 1:1 )
  • 30. Features of this approach• Geological units used as the primary variables• Operates in 3D• Integrates geological information from a 3D geological map with gravity and magnetic modelling• Any combination of gravity and magnetic data types – Scalar, vector or tensor components• Joint gravity and magnetic investigations• Properties sampled from statistical distributions• Induced susceptibility and remanent magnetisation• Conditional uncertainty estimates as well as parameter estimates – Stochastic approach (i.e., statistical or probabilistic method) generates many models that fit the data adequately
  • 31. Acknowledgements • Geoscience Australia • Intrepid Geophysics • BRGM • Teck Cominco • Nigel Phillips (UBC-GIF, Mira Geoscience) • Groups that have supported the development of GeoModeller • For further information, contact Richard Lane at Geoscience Australia (richard.lane@ga.gov.au)PDAC 2008