3D modeling of the Quest Projects Geophysical Datasets. Nigel Phillips

Transcription

1 3D modeling of the Quest Projects Geophysical Datasets Nigel Phillips Advanced Geophysical Interpretation Centre Undercover Exploration workshop KEG-25 April 2012

2 Mineral Physical Properties: density sus. cross-plot after Williams 2007

3 Rock Physical Properties: density sus. cross-plot What are the dominant causes of physical properties? Mineralogy Texture Grain size Porosity after Williams 2007

4 Rock Physical Properties Processes: density sus. cross-plot How do geologic processes change physical properties? Serpentinisation Mineralisation Metamorphism Weathering Igneous differentiation after Williams 2007

5 Inversion Essentials: What is Inversion? Data Measurements over the Earth are data.? Inversion processing Model Inversion estimates Earth models based upon data and prior knowledge. source: UBC-GIF

6 Inversion within it s proper context Energy from source Prior information Earth s physical properties Pre-processing Inversion. Measurements = Data Physical property distributions = MODELS

7 Inversion Theory Main objective: Choose a model that emulates geology and fits the data, but doesn t fit the noise in the data. 2 Challenges: There are an infinite number of possible models non-uniqueness How do we choose one? We don t know how noisy the data are.

8 Inversion Theory: Non-uniqueness Why an infinite number of models? The data (100 s 100,000 s values) are not sufficient to uniquely determine 10,000,000 s earth model parameters. Under-determined problem. The physical phenomena that we are exploiting (gravity, electromagnetic propagation) is usually a decaying as a function of depth or distance, and is not sufficient to uniquely describe the earth model.

9 Questions to consider: Consider the simple problem that involves two unknowns (model parameters), x and y. We have one datum, 2. x+y=2 What is the value of x and y? Infinite number of solutions Consider some candidate models for the x and y parameters: a: (0,2) b: (1,1) c: (2,0) d: (-1,3) Which one do we choose?

10 Using prior information to choose optimal models Encode prior knowledge in a form that can be optimized. i.e. build a mathematical rule or norm to test sizes of possible models, then choose the smallest. The people-in-the-room analogy: source: UBC-GIF

11 Questions to consider: Consider the simple problem that involves two unknowns (model parameters), x and y. We have one datum, 2. x+y=2 What is the value of x and y? Infinite number of solutions Consider some candidate models for the x and y parameters: a: (0,2) positive gradient b: (1,1) flattest c: (2,0) negative gradient d: (-1,3) has smallest value norms are described mathematically

12 How to pick one of infinitely many solutions? Narrow down the number of options using prior knowledge. Geophysical prior knowledge: Values are positive, and/or within bounds Physical Properties: Estimates for host rock properties Point-location values from drill hole information Logical prior knowledge: Find a simple result - as featureless as possible. This sacrifices resolution but prevents over-interpreting the data. Geologic prior knowledge: Character of the model (smooth, discontinuous) Some idea of scale length (or size) of the bodies Structural Constraints Challenge: Describe geology mathematically

13 Questions to consider: Consider the simple problem that involves two unknowns (model parameters), x and y. We have one datum, 2. But we really have: Sources of noise: x+y=2 x+y=2 (+/- unknown error) Instrument noise (sensitivity, accuracy, t 0 ) Location noise (GPS) Geologic noise near surface geology not of interest Modelling errors discretization limitations Operator mistakes Topography resolution

14 Inversion Methods Unconstrained Synthetic Example Potential Fields UBC-GIF GRAV3D and MAG3D Inversion for a smoothly varying heterogeneous 3D physical property distribution Recovers important information about subsurface: Depth to top of feature Centroid of feature Approximate physical property True Model Geological and physical property constraints Can be used to guide the result closer to the true earth solution. Recovered Model

15 Inversion Methods Constrained Synthetic Example Potential Fields UBC-GIF GRAV3D and MAG3D Inversion for a smoothly varying heterogeneous 3D physical property distribution Recovers important information about subsurface: Depth to top of feature Centroid of feature Approximate physical property True Model Geological and physical property constraints Can be used to guide the result closer to the true earth solution. Half of model constrained improves other half Recovered Model

16 Inversion models in context Geophysical inversions are non-unique and generated from noisy data. Be aware of this and use responsibly. Logical (non-geologic) constraints are a good starting point and add value to the data. Use prior information to further narrow down the range of suitable models. Common Earth Models Geologic models + Geochemical models + Geophysical models Honour all the data provide the most comprehensive, quantitative view of the subsurface.

17 Modelling Objectives Provide useful 3D physical property products For direct employment in regional exploration Provide guidance to the regional structure Help geologic mapping Help target prospective geology, alteration, or mineralization. Exploration criteria for different styles of mineralization can be applied based on multiple physical properties. Depth of overburden analysis Guide detailed follow-up survey design

18 Products 3D inversions of potential field data Interpolated 3D conductivity model based on 1D EM inversions Integrated 3D Physical Property Classification Models Accessible deliverables for visualization and quantitative 3D analysis Detailed infill areas

19 Mining Regions of BC and Regional Geophysical Survey Coverage

20 Summary of data Sanders airborne gravity GSC gravity compilation (Geotech magnetic) Aeroquest magnetic GSC magnetic compilation Geotech VTEM data Aeroquest AeroTEM data

21 Gravity Data Terrain Corrected Bouguer Anomaly (2.67 g/cm 3 ) mgal Airborne acquisition by Sander Geophysics East-West lines with 2000m line spacing Regional GSC data also used for regional signal

22 Magnetic Data Total Magnetic Intensity nt Airborne acquisition by Geotech and Aeroquest East-West lines with 4000m line spacing Regional GSC data also used for regional signal

23 Summary of Models Four large survey areas and 6 small infill areas: Bell, Endako, Equity, Huckleberry, Granisle, and Morrison. Potential Fields: 3D Density Contrast Model (UBC-GIF Grav3D) 3D Magnetic Susceptibility model (UBC-GIF Mag3D) 500m x 500m x 250m cells Tiled inversions (full compilation = ~100 million cells) Airborne EM Late time conductivity map 3D (interpolated) conductivity model (UBC EM1DTM) Depth of system penetration estimate Conductive Plates (EMIT Maxwell) GIS compilation in Gocad

24 Density Contrast Model 0m elev. g/cm 3

25 Density Contrast Model -2000m elev. g/cm 3

26 Density Contrast Model -4000m elev. g/cm 3

27 Magnetic Susceptibility Model 0m elev. S.I.

28 Magnetic Susceptibility Model -2000m elev. S.I.

29 Magnetic Susceptibility Model -4000m elev. S.I.

30 Density Contrast 3D isosurface cut-off 0.05 g/cc

31 Magnetic susceptibility 3D isosurface cut-off 0.05 S.I.

32 Prospective regions of High density contrast and high magnetic susceptibility.

33 Airborne EM Modelling 1D Inversions Inversion for a smoothly varying heterogeneous 1D conductivity distribution Laterally Constrained Inversion parameters are tuned to the changing geology Background/Late-Time Conductivity Depth of Investigation based on cumulative conductance Plate Modelling Alternative to the 1D interpretation for use when the layered earth assumption is inadequate.

34 Electromagnetic Data Channel 19 db z /dt data 27 time channels used ~78m flight height nt/s Airborne acquisition by Geotech (VTEM system) East-West lines with 4000m line spacing

35 Inversion Methods Background Conductivity: Plan View Airborne EM UBC-GIF EM1DTM Inversion for a smoothly varying heterogeneous 1D conductivity distribution Laterally Parameterized/Constrained Inversion Neighbouring stations used to determine appropriate inversion parameters Reduces modelling artefacts Background/Late-Time Conductivity S/m

36 AeroTEM modelling: Late-time Background Conductivity (Quest West)

37 Inversion Methods Airborne EM UBC-GIF EM1DTM Inversion for a smoothly varying heterogeneous 1D conductivity distribution Laterally Parameterized/Constrained Inversion Neighbouring stations used to determine appropriate inversion parameters Reduces modelling artefacts Background/Late-Time Conductivity Conductivity Flight-line Section Depth of Investigation based on cumulative conductance

38 Inversion Results Airborne EM Conductivity Model Fences shown through full 3D conductivity model Conformable with topography Log conductivity [S/m]

39 Inversion Results Airborne EM Conductivity Model Zoom Fences shown through full 3D conductivity model Conformable with topography Log conductivity [S/m]

40 Using the Results Quest Block C: Conductivity model: East-West flight line model sections Density Contrast iso-surface at a value of 0.05g/cm3 North-South magnetic susceptibility section

41 AeroTEM modelling: Huckleberry Mine Infill Area

42 AeroTEM modelling: Huckleberry Infill Area Stacked sections through 3D conductivity Model

43 AeroTEM modelling: Huckleberry Infill Area Stacked sections through 3D conductivity Model

44 AeroTEM modelling: Huckleberry Infill Area Conductivity Model - isosurfaces

45 AeroTEM modelling: Huckleberry Infill Area Conductivity Model - plates

46 Inversion Results Physical Property Classification Common 3D Discretization Mesh Each Physical Property classified into High, Medium, and Low Density Contrast and Magnetic Susceptibility Two-phase system 9 classifications Density Contrast, Magnetic Susceptibility, and Conductivity Background Three-phase system 27 classifications

47 Inversion Results 3D Physical Property Classification Surficial Plan View of 3D classification model

48 Inversion Results 3D Physical Property Classification Surficial Plan View of 3D classification model

49 Using the Results Regional Interpretation Integrated Interpretation Interpretation with Physical Properties Constraining Information Target Customization Survey Design Integrated Modelling Common Earth Model Development 3D GIS Regional Targeting Qualitative Quantitative

50 Using the Results Evaluation of physical property classification based on known mineral occurrences: 1. Compute spatial correlation of physical property classifications with known mineral occurrences in 3D. 2. Determine which class has the highest correlation. 3. Perform for Density Contrast and Magnetic Susceptibility two-phase system, and Density Contrast, Magnetic Susceptibility, and Conductivity Background three-phase system.

51 Using the Results evaluation of physical property classification Distance to known mineral occurrences.

52 Using the Results Den/Sus: Class 9 3D regions of interest

53 Using the Results Den/Sus/Con: Class 9 3D regions of interest

54 Targeting Workflow Establish clear exploration objectives and criteria Target generation from the Common Earth Model Target List X Y Z Mineral Potential Cube

55 Summary 3D density contrast, magnetic susceptibility, and conductivity models have been produced. The models provide more useful information than the data alone. While being aware of the limitations, the models can be used to promote detailed follow-up through 3D-GIS targeting analysis. The infill areas provide examples of what information can be extracted from these data. Introduce new information as it is acquired to test the validation of these models, and to help improve upon them as more focussed targets are resolved.

56 Acknowledgements Geoscience BC Team of Geophysicists at Mira Geoscience Advanced Geophysical Interpretation Centre UBC-GIF