The lithospheric scale view of an IOCG system

Stephan Thiel1,2

Kate Robertson1,2, Anthony Reid1,2

1Geological Survey of South Australia, Adelaide, Australia

2The University of Adelaide, Adelaide, Australia

2019 IOCG workshop

Adelaide, 2-3 December 2019

Acknowledgements

Lithosphere architecture of mineral systems

Griffin et al., 2013

Mineral systems – a thermodynamic problem with

threshold barriers

Hoggard et al, 2019

Lithospheric controls on copper deposits

doi: 10.31223/osf.io/2kjvc

Hoggard et al, 2019

Lithospheric controls on copper deposits

doi: 10.31223/osf.io/2kjvc

Cratonic root control on mineral deposits –Western US

Vp seismic tomography (90 km depth) vs mineral deposits

Griffin et al., 2013

Yellow: gold

Green: Cu-Au-Mo

Pink: Cu-Mo-Ag-Au

Orange: Mo

Light blue: REE

Dark grey: Fe

Dashed lines:

lithospheric blocks

Deposits concentrate

along lower velocity

regions or along

boundaries between

high and low velocities

Porphyry – Bingham porphyry

Afonso et al 2014

Porphyry – Bingham porphyry

Meqbel et al 2014

2D magnetotelluric resistivity models

Asthenospheric upwelling

Imprints of metasomatised mantle lithosphere

Skirrow et al., 2018

Possible techniques to decipher

mantle state

• Seismic tomography

• Isotope geochemistry

• Magnetotelluric data

• Constraints from laboratory

measurement under mantle

conditions

Conduction mechanisms associated with metasomatised mantle

11Department of State Development

• Ionic conduction: saline fluids and

melts (typically in tectonically active

settings), e.g. Afar rift, subduction zones

• Sulfides and magnetite in typically

crustal environments through shearing

and interconnection

• In stable lithosphere: hydrogen

diffusion in nominally anhydrous rocks

• Grain boundary graphite (only within its

stability field, Wang et al, 2013)

• Hydrated mantle minerals, e.g.

phlogopite (Li et al., 2016)

Olivine Pyroxene

Fullea, et al 2011

Resistivity images of mineral systems

IOCG and porphyry deposits

• Magmatic source

• Exsolved hydrothermal fluids

Ni-Cu and associated PGE deposits

• Magmatic source

• Crystallisation of sulphur-rich

magmas

• high T, high degree partial melting

of a metasomatised source

Share a similar mantle magmatic source ->

comparable primary architecture?

Imprints of metasomatised lithosphere in the Gawler Craton

Thiel et al., in prep Skirrow et al., 2018

Heterogeneous mantle source

14

Huang et al., 2016

• SCLM beneath Gawler

Craton likely enriched

(high in HFSE and REE,

Huang et al., 2016)

• Relation to fossil or active

supra-subduction at 1590

Ma (Wade et al., 2019)

Causes for enhanced electrical conductivity – fluorine?

Data source for plot:

GSSA data; Pankhurst 2006; Budd

2006; Creaser 1989; Salters &

Stracke 2004

Anomalously high phlogopiteconductivity

• Product of mantle metasomatism (e.g. peridotites

with slab-dehydrated fluids/melts)

• <100 Ωm @ 600 °C, ~ 1 Ωm @ 900 °C

• High abundance in K and F – main control for high

conductivity

Li et al., 2016

From lithospheric scale to deposit scale

Uncover 2012

Tasmania Model kindly provided by

Tom Ostersen, U Tas

The AusLAMP MT program

Skirrow et al 2018,

G3

Motivation – mapping the IOCG mineral system

• Relation to isotope geochemistry

Thiel et al., in prep

Cratonic root control on mineral deposits –South Australia

Copper occurrences and

resources occur on high

conductivity regions or

on the boundaries

between low resistivity

and high resistivity

Motivation – mapping the IOCG mineral system

• Previous high-resolution MT studies in the IOCG belt (Heinson et al., 2018, Sci Rep.)

Thiel et al., in prep

Heinson et al., 2018

Motivation – mapping the IOCG mineral system

The role of threshold barriers

Thiel et al., 2016

• Fluid pathways controlled by

mechanical barriers in the crust

• Bottom up approach to deformation

• Brittle-ductile feedback processes

develop mid-crustal detachment faults

• Inhibiting fluid flow across the brittle-

ductile boundary

• Results in zones of enhanced

conductivity and seismic reflectivity

(Connolly and Podladchikov, 2004,

JGR)

• breakthrough into the brittle domain

along discrete upper crustal faults

Connolly and

Podladchikov, 2004

Olympic Domain in-fill survey

• Funded by PaceCopper, South Australia

government initiative funding

• Tender process with Geoscience Australia

• Total of 334 BBMT and AMT stations (10-4 s -

~2000 s)

• Collected by Zonge in mid 2018, reprocessing

by CGG in Q2 2019

• Variable site spacing between 1.5 km and 5 km

• Grid dependent on road access, geographical

features (dry lake beds)

• Co-located airborne EM survey for cover

characterisation

Oak Dam

Maslin

Punt Hill

CarrapateenaKhamsin

Mount Gunson

Co-located airborne electromagnetic survey

• Co-located airborne EM survey for cover

characterisation

• Designed to follow E-W MT profiles

• Line spacing 1.5 km (around Carrapateena)

and 3 km

• Collected and processed by SkyTem

• QA/QC by Geoscience Australia

Olympic Domain in-fill survey

3D inversion of the entire grid

• 184 x 157 x 108 cells, 1634 x 1614 x 1470 km

• 750 m cell spacing

• Vertical cells 5 m increasing with depth

• Periods from 0.0001 s to 2000 s

• Tests of different starting half-spaces and

covariances

• inversion are still on-going

• 4% error for Zxy and Zyx component of

impedance tensor

• 8% for Zxx and Zyy component of impedance

tensor

• Tipper error floors of 0.02

Data misfit 400 s period

3D inversion of the entire grid

300 m depth 1800 m depth

Resistive upper crustal structure (1850 Ma, Donington Suite) controls main

basement structure

4000 m depth Interpreted solid geology

Detailed 2D profiles

Oak Dam

Maslin

Punt Hill

CarrapateenaKhamsin

Emmie Bluff

Elizabeth Creek

Fault

• Profile selection based on deposits and

dimensionality/strike of data

• 200 m horizontal cell spacing

• Vertical cells 5 m increasing with depth

including topography

• Periods from 0.0001 s to 2000 s

• TE (50% and 7% error, 𝜌𝑎 and 𝜙), TM (6% and

3% error, 𝜌𝑎 and 𝜙), and Hz data (0.01 error

floor)

• inverted using Non-linear conjugate gradient

approach (Rodi and Mackie, 2001; Geotools)

1

3

2

RMS = 1.64

Carrapateena – Khamsin profile (E-W)

CarrapateenaKhamsin

RMS = 1.84

Maslin – Punt Hill profile (SW-NE)

RMS = 1.64

Punt Hill

MaslinArea of Cu-Au skarn alteration

(Fabris et al., 2018)

RMS = 1.64

Oak Dam profile (E-W)

RMS = 1.42

Oak Dam West

Airborne EM draped on topography

Vertical exaggeration 20x

Airborne EM draped on topography2 - 6 m depth 143 - 159 m depth

Vertical exaggeration 20x

Conclusions

Acknowledgments

• South Australian AusLAMP data used in modelling were funded by initiatives of the Department for Energy and Mining and NCRIS, Geoscience Australia.

• Model files were generated and viewed using MTpy software (Kirkby et al, 2019) and 3D grid software from Naser Meqbel. Some figures were drawn using the Generic Mapping Tools (Wessel and Smith, 1998).

• This work was supported by computational resources provided by the Australian Government through the University of Adelaide under the National Computational Merit Allocation Scheme.

• We thank traditional owners and landholders for granting access to their land. We also acknowledge the field team and technical team that made data acquisition possible.

• Many sites were collected using instruments from the AuScope Instrument Pool

Contacts

Dr Stephan Thiel

Program Coordinator- Lithospheric Architecture

Department for Energy and Mining

11 Waymouth StreetAdelaide, South Australia 5000

GPO Box 320Adelaide, South Australia 5001

E: Stephan.Thiel@sa.gov.au

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