Understanding and Addressing Systemic Uncertainties in Geoscientific Data Interpretation

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1 Understanding and Addressing Systemic Uncertainties in Geoscientific Data Interpretation Associate Professor Eun-Jung Holden Geophysics and Computational Analysis Theme The Centre for Exploration Targeting (CET) The University of Western Australia Contributors: Yathunanthan Sivarajah, Peter Kovesi, Jason Wong, Daniel Wedge, Tom Horrocks

2 MOTIVATION Geoscientific data interpretation is uncertain (Frodeman, 1995) o Combine multiple, often ambiguous, incomplete, inaccurate and low resolution observations o Human biases based on interpreters presuppositions and expectations of the task Well known seismic interpretation assessment (412 interpretations) by Bond et al. (2007) using synthetic data o 21% interpreters successfully identified the tectonic setting o 23% identified the three major faults Bond, C., Gibbs, A., Shiptonand, Z, Jones, S., 2007, What do you think this is? Conceptual uncertainty in Geoscience Interpretation, GSA Today, 17:11, pp

3 Inattentional Blindness X-ray computed tomography (CT) scan of a human lung & the task was to find bright white cancer nodules (Drew, 2013) Drew, T., Võ, M.L.H, and Wolfe, J.M., The Invisible Gorilla Strikes Again: Sustained Inattentional Blindness in Expert Observers Psychological Science, September : Dancing gorilla was hidden in 239/1000 scans - 4 out of 24 radiologists spotted the gorilla Lesson: It s important to be willing to look for more than one thing, to set yourself up for success.

4 Inattentional blindness is more problematic for geological interpretation due to different features sought at once, complexity of feature characteristics, etc. Our approach: Amplify our analytical reasoning and intuition and minimise biases through visual interactive interfaces VISUAL ANALYTICS

5 Visual Analytics Thomas, J., Cook, K.: Illuminating the Path: Research and Development Agenda for Visual Analytics. IEEE-Press (2005)

6 VISUALISATION to view multi-data or multi-level information within single data for interpretation

7 Magnetic Geophysics Data Spatial distribution of magnetic susceptibility of the subsurface Economical and useful data for subsurface geological mapping Magnetic data - courtesy of Fugro Airborne Surveys Pty Ltd.

8 Conventional Visualisation Magnetic data - courtesy of Fugro Airborne Surveys Pty Ltd.

9 Conventional Visualisation Magnetic data - courtesy of Fugro Airborne Surveys Pty Ltd.

10 Conventional Visualisation Magnetic data - courtesy of Fugro Airborne Surveys Pty Ltd.

11 Enhancement Problems Unlike digital photographic images, geoscientific data representing physical measurements can have a large dynamic range o Histogram equalisation enhances contrast by over-stretching the data range with high occurrence frequencies poor sense of overall contrast Colour mapping Displaying high dynamic range data with a limited number of colours/shades For geological structural interpretation, what is the best display method?

12 Impression, soleil levant (Sunrise) by Claude Monet Grayscale display of the paining

13 HUMAN VISUAL SYSTEM Photoreceptor cells ~120 million rods Sensitive to low light levels, responsible for night vision ~6 to 7 million cones Packed densely near fovea Colour vision, colour sensitivity, and fine detail

14 The cone receptors in our eyes do not sample the spectrum evenly... The sensitivity of rods and cones to light of different colour

15 Our perception of brightness varies with hue... Relative brightness sensitivity of the human visual system as a function of wavelength org/wiki/color_visi on

16 Mean wavelength discrimination curve (From Davson, H., The Eye, Vol 2. London Academic Press, 1962), Source:

17 Psychological Colourmap (Welland et al. 2006) Relative amplitude differences are better represented in psychological colour bar than the conventional colour bar A comparison of data displayed using a conventional color bar (upper) and one derived from psychological color space (lower). Data courtesy of Ikon Science.

18 Grey vs Colour Provided by Jessell

19 Greyscale Data Display Grayscale display is effective in identifying contrasts but difficult to recognise absolute intensity! Provided by Jessell

20 Gray Scale LUT Rainbow LUT Contours Provided by Jessell In gray, difficult to establish absolute differences in value for different light areas, but emphasise high frequency information In colour, easily see the different zones of the same intensity or differences in intensity In contour image, spatial gradients are visible

21 For Structural Interpretation Local contrast detection is more important High frequency filters are often used Use of grayscale to display high frequency filter outputs may be useful to detect local contrasts/discontinuities Due to high dynamic range of potential field data, mapping of this range into a limited number of shades in gray (or colour) may led to difficulty in recognising features

22 Histogram equalised Phase Preserving Dynamic Range Compression (DRC) (Kovesi, 2013) Enhancement of data with a high dynamic range to allow effective grayscale display A method combining high pass filtering & tone mapping Dynamic Range Compression

23 + = Phase and amplitude mixed image (Oppenheim and Lim 1981) Dynamic Range Compression (Kovesi, 2013) Commercialised through CET Grid Analysis Extension for Geosoft Oasis Montaj Obtain local phase and amplitude of signal at each point in the image. Apply a range reducing function (e.g. logarithm) to the amplitude values Reconstruct image with the original phase and the reduced amplitude values.

24 Geological interpretation is about understanding 3D geology using multiple types/levels of information within data o For potential field data, different frequency levels are used to analyse anomalies associated with causative sources at different depths o Full tensor gradient gravity has multiple independent components which need to be combined for interpretation o Different datasets such as gravity and magnetics can be interpreted together to understand deep geology

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31 How to amplify intuition and analytical reasoning when viewing multi data? Detection of motion, specifically appearance of new object draws attention (Hillstrom & Yanties, 1994)

32 Interactive Multi-Image Blending for Data Visualisation and Interpretation (by Peter Kovesi, Eun-Jung Holden, Jason Wong) won the Laric Hawkins Memorial Innovation Award at the 23rd International Geophysics Conference and Exhibition in Melbourne, blended image = W 1 image 1 + W 2 image 2 (W 1 + W 2 = 1) Image 1 Image

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34 Multi-bilinear Blending parameter 2 parameter 1 Bilinear blending between the four images around the cursor position.

35 Image Blending Tools frequencies gravity magnetic Magnetic data - courtesy of Fugro Airborne Surveys Pty Ltd.

36 Blending 3 images

37 More than 4 Images: The Image Wheel Data: Fugro Airborne Surveys Pty Ltd.

38 Integrated Exploration Platform A new major research initiative ( ) between Geological Survey of WA and CET through WA Exploration Incentive Scheme (EIS) phase 2 Australian Research Council (ARC) linkage grant Reducing 3D Geological Uncertainty via Improved Data Interpretation Methods Visualisation and other interpretation assistive tools will be made available for explorers in WA to promote full utilisation of GSWA data

39 AUTOMATED DATA ANALYSIS to amplify analytical reasoning and minimise biases Thomas, J., Cook, K.: Illuminating the Path: Research and Development Agenda for Visual Analytics. IEEE-Press (2005)

40 CET Tools 2013 CET Grid Analysis Status Commercial products & on-going upgrade Commercial in progress Recently completed Projects Geophysics potential field data analysis (Barrick Gold) Downhole televiewer image analysis software (Rio Tinto) Drill core grain size analysis (Woodside) CET Porphyry Detection Recently completed Automated ore sorting (AngloGold Ashanti) Ongoing UAV photogrammetric data based structure detection (UWA & CET scholarship) STONY COAL CARBONACEO US MUDSTONE CARBONACEO US SILTSTONE Recently completed Downhole geophysical log analysis for coal strata detection (Conducive, student support) SOIL SANDSTONE MUDSTONE SILTSTONE Ongoing Gravity tensor visualisation (UWA funded, collaborating with First Quantum Minerals Ltd) SAND GRAVEL COAL CLAY Precision Recall

41 CHALLENGE: Developers need to understand that automated engine is to support human in decisions, NOT making human cleaning up automated results

42 Semi-automated geological mapping using UAV captured data Oktokopter fitted with Canon 550D Dense point cloud DEM Survey was flown by Darren Turner & Arko Lucieer from University of Tasmania

43 Semi-automated Geological Structure Mapping Visual Interpretation Automatic line detection Semi-automatic Interpretation using user input Vasuki, Y., Holden, E.-J., Kovesi, P., Micklethwaite, S., Semi-automatic mapping of geological Structures using UAV-based photogrammetric data: An image analysis approach. Comput. Geosci. 69, doi: /j.cageo

44 Semi-automated Lithology Mapping Manual interpretation

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50 Wireline Spelunker Team 50 Grades of Shale Tom Horrocks, Jelena Markov, Daniel Wedge, Chris Gonzalez, Yathunanthan Sivarajah, Eun-Jung Holden The Centre for Exploration Targeting (CET), School of Earth & Environment, The University of Western Australia

51 As surface riches run out, we are challenged with the mapping of deeper lithology of Earth... Petrophysical measurements from deep drilling will play a key role in geological mapping for cost-effective deep exploration and mining As a new hole is drilled, can we update the model in real-time? Automated lithology classification and correlation are the key component Colour lithology surface identified by Wireline Spelunker and visualised by 3D Discover using 0.1m resolution downhole geophysics Mesh surface is based on human core logging at 2 m resolution Wireline Spelunker classifies, correlates and visualises petrophysics data

52 Lithology Classification Neural Network

53 Log values for one depth interval Range Transformation GRDE [-1,1] CODE [-1,1] ADEN [-1,1] COAL [0,1] SHALE [0,1] Lithology Similarity

54 Gamma Log Correlation Training Set Generation Pattern Matching Gamma Log Desired Confidence Gamma Log Confidence Neural Network

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56 Wireline Spelunker Realisation of real-time processing of downhole data for efficient and cost-effective mine-site workflow Innovative use of machine learning based pattern matching in detecting and correlating geological zones Demonstrated value in fully utilising high resolution downhole geophysics to avoid time consuming and subjective human data analysis Enhanced decision support through an intuitive interface to utilise automated analysis outcomes

57 Acknowledgements The aeromagnetic data for the Yilgarn Craton (Group A Menzies to Norseman Nonexclusive Database) belongs to Fugro Airborne Surveys Pty Ltd. Permission to reproduce images of these data is gratefully acknowledged.