Utilising geological uncertainty: imaging under cover {in

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?



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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)




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




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)


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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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Application – Yerrida Basin (no PR)

Giraud et al. 2020

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Application – Yerrida Basin (with PR)

Giraud et al. 2020

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(a)

(b)

Suggested by geology alone

After SOM classification



(1) Goodin inlier and back(2) Non-mafic greenstone(3) Mooloogool group(4) Juderina(5) Mafic greenstone(6) Killara


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


∅ ∅ ∅ ∅ ∅ ∅∅ ∅ ∅ ∅ ∅

∅ ∅ ∅ ∅∅ ∅ ∅

∅ ∅∅



Automated interp.: 3D geol. image of subsurface

Geological uncertaintyMost probable lithology

Inverted modelSpatial variations of inverted

modelAverage petrophysical model

(b)

After SOM classification




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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:


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[ ]−∞ +∞

Common sense

… upper and lower bounds:


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[ ] [ ] [ ]−∞ +∞ Elementary:

2D map

… intervals defined globally:


Ogarko et al. 2020

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[ ] [ ] [ ][ ] 3D geological uncertainty info


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.

http://www.earthexplorer.com/2013/images/VOXI-3Dmap.jpg

http://explorationgeophysics.info/?cat=8

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Thank you for your attention

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Utilising geological uncertainty: imaging under cover {in