Jeremie Giraud, Mark Lindsay & Mark Jessell, Vitaliy Ogarko, Roland Martin
Centre for Exploration Targeting, University of Western Australia
17th – 20th February 2020
Utilising geological uncertainty: imaging under cover
{in:: geophysics}
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Surface data
Cover of varying thickness
Images can be found online in webpages given in refs.
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Affected by uncertainty, need to integrated disciplines
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Area location (modified from¹)
Q: How does it dip? How sure are we?
¹ Pirajno et al. 1998² Pirajno and Occhipinti 2000
Integrated workflow: 3D Geology, Petro, Geophysics Quantification of uncertainty and risk
Known deposits possible deposits
cross-section modified from ²
cover
?
? ??
Economic minerals
Basin
Greenstones
6Modified from Lindsay et al. 2019, Pirajno et al. 1998, Giraud et al. 2020
Target- Dipping mafic Greenstone:190 < Density contrast < 270 kg/m³
Data• Geological measurements (~500)• Gravity data (~5000 points)• Petrophysical info (samples)
Yerrida Basin
?
? ?
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Target- Dipping mafic Greenstone:190 < Density contrast < 270 kg/m³
Data• Geological measurements (~500)• Gravity data (~5000 points)• Petrophysical info (samples)
N
(c)
(a)
(b)
Yerrida Basin
Modified from Lindsay et al. 2019, Pirajno et al. 1998, Giraud et al. 2020
Method I – classification of multidisciplinary dataset• Geological uncertainty • Geophysical inversion results
Method II – geophy. inversion with geol. uncertainty • Geophysics/geological uncertainty/petrophysics
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Utilising geological uncertainty
Q.: How to account for geological uncertainty in identification of rock units in 3D, with other disciplines?
Q.: How to reconcile geological uncertainty and geophysical inversion?
Method I – classification of multidisciplinary dataset• Geological uncertainty • Geophysical inversion results
Method II – geophy. inversion with geol. uncertainty • Geophysics/geological uncertainty/petrophysics
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Utilising geological uncertainty
Q.: How to account for geological uncertainty in identification of rock units in 3D, with other disciplines?
Q.: How to reconcile geological uncertainty and geophysical inversion?
Orientation data, contacts
Measurement with uncertainty (dip, strike, contact…)
Samples
±𝟐𝟐±𝟐𝟐.𝟓𝟓
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Combining all realisations
≠ from best guess model:Probability of observing lithology
𝑝𝑝𝑘𝑘,𝑖𝑖
Monte Carlo sampling of geological model space
Set of geologically plausible models.… ….
Estimating Geological uncertainty
Can be used to inform imaging
classification of multidisciplinary dataset - principle
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Lithology model
Geophysical response
Petrophysicalproperties
geophysical inversion-derived model(s)
Earth
Lithology classification
For a review of geology differentiation: Li et al. 2019, and classification: Bergen et al. 2019.
Geological measurements
?
Prior info(geol laws)
Geological model(s) and uncertainty
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Methodology – classification (training)
Geological field measurements
Geological model uncertainty Geophysical inversion
features for lithological classification
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Classification using Self-Organizing Maps¹ (SOM)
Methodology – classification (training)
Geological field measurements
Geological model uncertainty Geophysical inversion
features for lithological classification¹ Kohonen (1982), Vatanen (2015).
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Classification using Self-Organizing Maps¹ (SOM) Training & validation using geophy. & geol feasibility study Classification applied to field data
Methodology – classification (training)
Geological field measurements
Geological model uncertainty Geophysical inversion
features for lithological classification¹ Kohonen (1982), Vatanen (2015).
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Classification using Self-Organizing Maps¹ (SOM) Quantities used
oReference lithological model 𝑙𝑙oGeological uncertainty 𝑊𝑊𝐻𝐻oAverage petro. model 𝑚𝑚𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠oSpatial variations in inverted modeloInverted model
Methodology – classification
Geological modelling/uncertainty
Geophysical inversion
¹ Kohonen (1982), Vatanen (2015).
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Reducing classification uncertainty: geological context
Neighbourhood
Classification respects (geological)
‘topological’ rules
𝜙𝜙
𝜙𝜙
𝜙𝜙
𝜙𝜙Lith
o. in
dex
Litho. index1 2 3 4
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Contacts
Forbidden Allowed
¹Tarabalka et al. (2009), Stavrakoudis et al. (2014), Cracknell and Reading (2015), Giraud et al., 2020.
Geological plausibility through postregularization¹ (PR)
oadjacency matrix – contacts between cells (3D)
Prior info(geol laws)
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Application to Yerrida Basin undercover imaging
Particular interest in greenstones.
??
???
Entropy: geological uncertainty metric (calc from probabilities)
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forbidden
∅ ∅ ∅ ∅ ∅ ∅∅ ∅ ∅ ∅ ∅
∅ ∅ ∅ ∅∅ ∅ ∅
∅ ∅∅
Goodin inlierFelsic greenstone
Mooloogool group
Juderina formation
Mafic greenstone
Killara
Topological rules
allowed
+ no single-cell inclusions
Goodin inlier and backgroundNon-mafic greenstoneMooloogool groupJuderinaMafic greenstoneKillara
( )Reference geological model used for probabilistic simulationsInverted model
Spatial variations of inverted model
Average petrophysical model
Features and info for classification
Giraud et al. 2020
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Adjacency matrix
(1) Goodin inlier and background(2) Non-mafic greenstone(3) Mooloogool group(4) Juderina(5) Mafic greenstone(6) Killara
Lithologies with index
(b)
No forbidden contacts
Geol Postregularization
Classification, no PR
Classification, PR
forbidden
Classification – with and without geological PR
Giraud et al. 2020
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(1) Goodin inlier and background(2) Non-mafic greenstone(3) Mooloogool group(4) Juderina(5) Mafic greenstone(6) Killara
Lithologies with index
Application – Yerrida Basin (no PR)
Giraud et al. 2020
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(1) Goodin inlier and background(2) Non-mafic greenstone(3) Mooloogool group(4) Juderina(5) Mafic greenstone(6) Killara
Lithologies with index
(1) Goodin inlier and background(2) Non-mafic greenstone(3) Mooloogool group(4) Juderina(5) Mafic greenstone(6) Killara
Lithologies with index
Application – Yerrida Basin (with PR)
Giraud et al. 2020
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(a)
(b)
Suggested by geology alone
After SOM classification
Goodin inlier and backgroundNon-mafic greenstoneMooloogool groupJuderinaMafic greenstoneKillara
Goodin inlier and backgroundNon-mafic greenstoneMooloogool groupJuderinaMafic greenstoneKillara
(1) Goodin inlier and back(2) Non-mafic greenstone(3) Mooloogool group(4) Juderina(5) Mafic greenstone(6) Killara
Lithologies with index
A
B
C
A
B
C
Geology alone
Classification fromGeol. uncertainty & Geophy.
Focus on Greenstones: (re)interpretation
??
???
Giraud et al. 2020
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Conclusion method I
Lithology model
Geophysical response
Petrophysicalproperties
Integrated geophysical inversion-derived model
Earth geological model(s)
Lithology classification
Geological uncertainty
Prior info(geol laws)
∅ ∅ ∅ ∅ ∅ ∅∅ ∅ ∅ ∅ ∅
∅ ∅ ∅ ∅∅ ∅ ∅
∅ ∅∅
(1) Goodin inlier and background(2) Non-mafic greenstone(3) Mooloogool group(4) Juderina(5) Mafic greenstone(6) Killara
Lithologies with index
Automated interp.: 3D geol. image of subsurface
Geological uncertaintyMost probable lithology
Inverted modelSpatial variations of inverted
modelAverage petrophysical model
(b)
After SOM classification
Goodin inlier and backgroundNon-mafic greenstoneMooloogool groupJuderinaMafic greenstoneKillara
Method I – classification of multidisciplinary dataset• Geological uncertainty • Geophysical inversion results
Method II – geophy. inversion with geol. uncertainty • Geophysics/geological uncertainty/petrophysics
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Utilising geological uncertainty
Q.: How to account for geological uncertainty in identification of rock units in 3D, with other disciplines?
Q.: How to reconcile geological uncertainty and geophysical inversion?
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Yerrida Basin undercover imaging
Particular interest in greenstones.
??
???
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Use in geophysical inversion (example. of density contrast)Range of considered density contrast value
−∞ +∞No use
Black holeNegative density particles
Hydrogen Iridium[ ]
−∞ +∞Common sense
(geophysicist-level)
Usage of geol and uncertainty
[ ] [ ] [ ]−∞ +∞
Elementary - 2D mapObserved rock 1 Observed rock 2 Observed rock 3
[ ][ ] [ ] [ ] OK - 3D probabilistic model – with uncert.
Rock units to choose from vary in space accordingly with geological uncertainty
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Uncertainty information available: proba. of rock unit
Goodin inlier and background (0 kg/m³)
Non-mafic greenstone (30 kg/m³)
Mooloogool group (130 kg/m³)
Juderina (180 kg/m³)
Mafic greenstone (230 kg/m³)
Killara (330 kg/m³)
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Uncertainty information available: proba. of rock unitBlue – everywhere a given rock unit’s probability is > 0
Goodin inlier and background (0 kg/m³)
Non-mafic greenstone (30 kg/m³)
Mooloogool group (130 kg/m³)
Juderina (180 kg/m³)
Mafic greenstone (230 kg/m³)
Killara (330 kg/m³)
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Uncertainty information available: combination and cardinality
Define possible values depending on rock units probabilities Number of rock types allowed in each cell
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Interval number Cardinality
Different domains of allowed rock units (ex.: rock 1 and 3 only allowed, rock 1-2-4 allowed, etc.)
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Considered density contrast value scale−∞ +∞No use
… no info used:
Geophysical inversion
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[ ]−∞ +∞
Common sense
… upper and lower bounds:
Geophysical inversion
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[ ] [ ] [ ]−∞ +∞ Elementary:
2D map
… intervals defined globally:
Geophysical inversion
Ogarko et al. 2020
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[ ] [ ] [ ][ ] 3D geological uncertainty info
Geophysical inversion
Direct mapping back to rock units. … and linking back to cardinality:
Entropy: geological uncertainty metric (calc from probabilities)
… using geological uncertainty defining domains for spatially varying intervals:
Ogarko et al. 2020
Does not contradict info from geol
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Summary method II Example of mapping rock types undercover using geology and
geophysicsReconcile geological uncertainty and geophysicsRefine results from geological uncertainty modelling and geophysics Discriminate between rock units when several are allowed
Hints for exploration targeting Flexible domaining is flexible
[ ] [ ] [ ][ ]
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Conclusion and discussion
Pushed eastwards
Thinner and potentially shallower
Potentially broken in two, intruded by deeper body More data needed to confirmUpdate geological model
No connection between the two
Results TBC with more modelling.
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Conclusion and discussion
Other techniques investigated MT + potential data for depth to basement
Simultaneous geological / geophysical modellingSeismic with gravity (PhD student, Mahtab Rashidi Fard) MT + passive seismic (PhD student, Nuwan Suriyaarachchi )
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Image geophy: http://www.earthexplorer.com/2013/images/VOXI-3Dmap.jpg, http://explorationgeophysics.info/?cat=8References
Pirajno F., Occhipinti S. A. and Swager C. P. 1998: geology and tectonic evolution of the Paleoproterozoic Briyah and Padbury Basins, Western Australia: Western Australia Geological Survey, Report 59, 52p.
Pirajno F. and Occhipinti S. A. 2000: Three Palaeoproterozoic basins – Yerrida, Bryah and Padbury – Capricorn Orogen, Western Australia, Australia Journal of Earth Sciences, 47, 675-688.
Bergen, K. J., P. A. Johnson, M. V. de Hoop, and G. C. Beroza, 2019, Machine learning for data-driven discovery in solid Earth geoscience: Science.
Cracknell, M. J., and A. M. Reading, 2015, Spatial-Contextual Supervised Classifiers Explored: A Challenging Example of Lithostratigraphy Classification: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 1–14.
Giraud, J., Lindsay, M., Jessell, M., and Ogarko, V.: Towards geologically reasonable lithological classification from integrated geophysical inverse modelling: methodology and application case, Solid Earth Discuss., https://doi.org/10.5194/se-2019-164, in review, 2019.
Tarabalka, Y., J. A. Benediktsson, and J. Chanussot, 2009, Spectral–Spatial Classification of Hyperspectral Imagery Based on Partitional Clustering Techniques: IEEE Transactions on Geoscience and Remote Sensing, 47, 2973–2987.
Kohonen, T., 1982, Self-organized formation of topologically correct feature maps: Biological Cybernetics, 43, 59–69.Lindsay, M., Occhipinti, S., Laflamme, C., Aitken, A., and Ramos, L.: Mapping undercover: integrated geoscientific interpretation and
3D modelling of a Proterozoic basin, Solid Earth Discuss., https://doi.org/10.5194/se-2019-192, in review, 2020.Ogarko V., Giraud J., Martin R., and Jessell M., 2020, Disjoint interval bound constraints using the alternating direction method of
multipliers (ADMM) for geologically constrained geophysical inversion, in rev. Geophysics.
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Thank you for your attention