Prospectivity analysis of the Halls Creek Orogen, Western Australia using a mineral systems approach Occhipinti, S. A.1, Metelka, V.1,*, Lindsay, M. D. 1, Hollis, J. 2, Aitken, A. R. A. 1, Sheppard, S.3, Orth, K.4, Tyler, I. M. 2, Beardsmore, T. 2, Hutchison, M.2, & Miller, J. M.1 (1. Centre for Exploration Targeting, The University of Western Australia, M006, 35 Stirling Highway, Crawley, WA 6009, Australia.; 2. Geological Survey of Western Australia, Department of Mines and Petroleum,100 Plain Street, East Perth, WA 6004, Australia.; 3. Brockman Mining, Level 1, 117 Stirling Hwy, Nedlands, WA 6009, Australia.; 4. Centre for Ore Deposit Research and School of Earth Sciences, University of Tasmania, Private Bag 79, Hobart, Tasmania 7001, Australia.; * Corresponding Author) Introduction

The aim of this study was to examine and document a series of generic mineral deposit types and ascertain their differences and similarities, and then apply a semi-automated approach to mineral systems and prospectivity analysis (prediction) at the regional scale in the Halls Creek Orogen. The work drew heavily on compilations of different mineral deposits by many authors including, but not restricted to (Groves and Bierlein, 2007, Gurney, 2010, Leach, 2010); however it placed these genetic models into a Minerals Systems context – where it is recognised that deposits formed in different tectonic settings, or under different conditions are often found in the same region (Mc Cuaig and Hronsky, 2014). Leading on from this work, GIS-based prospectivity models were constructed using the well-described method of applying the mineral systems approach to a fuzzy inference network (e.g. Joly et al., 2012, Lindsay, M., et al., submitted).

Mineral systems and geology

The geological evolution of the Kimberley region (Fig. 1) spans more than 2 billion years and involves periods of accretion, convergence and rifting along a margin affected by changing tectonic environments, dividing the orogen into the western, central and eastern terrane (Tyler et al. 1995). This led to the development of a region in which rock types and mineral systems that formed in different settings are adjacent. Contact relationships between rock units are varied. We have updated GSWA geological maps (Fig. 1), adding additional information to the attribute table in order to facilitate the mineral systems analysis (Fig. 2)

The Halls Creek Orogen hosts a variety of mineralisation styles. Operating mines in the Halls Creek Orogen include: Ridges Iron Ore, Savannah (nickel and platinum group elements) and Argyle (diamonds). However, the area has been the source of many different styles of minerals deposits in the past, including Au in the Eastern zone, and Pb, Cu, Ag, Au, Sb in the Central and Western zones.

Prospectivity map (fuzzy product)

0.9 x Common fertility elements

(fuzzy OR)

Favorable lithological units x 0.9

0.7 x Lithospheric architecture (fuzzy SUM)

Mantle tapping faults x 0.9

Mantle taping fault intersections

X 0.9

0.5 x Geodynamic throttle

(fuzzy OR)

Fault bends density x 0.8

Structural complexity x 0.8

0.5 x Depositional site

(fuzzy OR) Chemical contrast

X 0.8

Rheological contrast x 0.8 0.5 x Preservation

(fuzzy OR)

Metamorphic zones X 0.9

www.cet.edu.au

We apply the minerals system approach to predict a variety of resources (Au, Cu, Mo, Ni, PGE, V, Ti, Zn, Pb, Sn, W, REE, and diamonds). The key to the approach was to understand the basic elements required in order to form and preserve a mineral deposit (Fig. 3).

We have derived 70 predictor maps (Fig. 5a) from geological maps, GSWA observations, geophysical interpretation layers and primary geophysical data. Seven prospectivity models (Figs. 6-12) partitioned by mineral/element associations were created: Au (6), Cu-Au-Mo (7), Pb-Zn-Cu-Ag (8), Ni-Cu-V-Ti-PGE (9), Sn-W-Mo (10), diamond (11) and REE (12). The models combine the predictor maps through a set of fuzzy operators to produce a final prospectivity map.

All of the models were constructed within the ESRI ArcToolbox ModelBuilder (Fig. 4) environment and can be easily rerun with different map weights or fuzzy operators (Fig. 5b). The prospectivity toolbox will be included in the final GSWA data package for distribution.

Prospectivity analysis

The selected fuzzy logic-based inference network models (Fig. 5) require several predictor maps to be assigned member-ship values based on a combination of objective datasets and subjective model components Each predictor map represents a component of a particular mineral system .

Acknowledgements Our work in the east Kimberley has benefitted from fruitful discussion with many working in the region. In particular we would like to thank: Marco Fiorentini, Cam McCuaig (CET/UWA); Ken Rogers (King River Copper Pty Ltd), John Hicks, Andrew Shaw-Stuart (Panoramic Resources), Karin Orth (University of Tasmania), Sidi Morin-Ka, Chris Phillips, John Brett & Dave the Fieldy (GSWA).

References Groves, D.I., Bierlein, F.P., 2007. Geodynamic settings of mineral deposit systems. Journal of the Geological Society of London, 164: 19-30. Gurney, J.J., Helmstaedt, H.H., Richardson, S.H., and Shirley, S.B., 2010. Diamonds through time. Economic Geology, 105: 689-712. Joly, A., Porwal, A. and McCuaig, T.C., 2012. Exploration targeting for orogenic gold deposits in the Granites-Tanami Orogen: Mineral system analysis, targeting model and prospectivity analysis. Ore Geology Reviews, 48: 349-383. Leach, D.L., Bradley, D.C., Huston, D., Pisarevsky, S.A., Taylor, R.D., and Gardoll, S.J., 2010. Sediment-hosted lead-zinc deposits in Earth History. Economic Geology, 105: 593-625. Lindsay, M., Aitken, A.R.A., Ford, A., Dentith, M., Hollis, J., Tyler, I., submitted. Mineral prospectivity in the west Kimberley. Ore Geology Reviews. McCuaig, C. and Hronsky, J.,2014. The Mineral System Concept: The Key to Exploration Targeting, Society of Economic Geologists Special Publication, Chp 8. Tyler, I. M., Griffin, T. J., and Shaw, R. D., 1995. Are there terranes within the Lamboo Complex of the Halls Creek Orogen?: Geological Survey of Western Australia, Annual Review 1993-94, v. 1993/94, p. 37-46.

The resulting maps (Figs. 6-12) capture most of the relevant records from the MINDEX database within prospective regions, but suggest also new areas that should be considered as favorable exploration targets.

Results

Fig. 5a Fig. 5b

Fig. 4

Fig. 3

Fig. 2

Fig. 1

The mineral systems diamond

Updated geological map

Attribute table for geology

ArcToolbox model

Evidence layers Fuzzy overlay Prospectivity

Fig. 6

Fig. 7

Fig. 8 Fig. 9 Fig. 10

Fig. 11

Fig. 12

Prospectivity Au

Prospectivity Cu, Au, Mo

ProspectivityNi, Cu, V, Ti, PGE

Prospectivity Zn, Pb, Cu, Ag

Prospectivity Sn, W, Mo

Prospectivity diamonds

Prospectivity REE