automated prospectivity analysis for intrusion-related ...€¦ · automated prospectivity analysis...

Automated prospectivity analysis for intrusion-related mineral systems in the Charters Towers-Ravenswood region

Arianne Ford

2

Outline

• Aims and scope • Mineral system models • Data • Prospectivity mapping

Aims and Scope

• Evaluate available data • Develop targeting criteria for intrusion-

related mineral systems – Specific focus on geochemistry

• Generate prospectivity maps • Develop a method for ranking existing

targets

Study Area

Mineral Systems

• Deposits are focal points of much larger mass flux and energy systems

• Focus on critical processes that must occur to form a deposit

• Allows identification of mineralization processes at all scales

• Not restricted to particular geologic settings/deposit type

Charters Towers-Ravenswood Mineral Systems

• Siluro-Devonian – Intrusion-hosted orogenic gold system – Mineralization hosted in Silurian granites – Mineralization dates are Devonian – ?Source?

• Permo-Carboniferous – Intrusion-related gold system – Mineralization related to sub-volcanic intrusions

of Kennedy Igneous Association

Data

• Geochemistry • Geology • Geophysics

Geochemistry • Terra Search/GSQ data

– SSS; RC; Soils – Lots of data points

• limited multi-element analysis • Patchy spatial distribution

– Works well for mapping out the Ravenswood Batholith using automated methods

• Carpentaria Gold (Resolute) data – RC and Soils have good sample density over

Ravenswood district – Good multi-element geochemical analysis

Geochemistry maps

Carpentaria Gold Geochem

Geochemistry – data analysis

• Focus on the data provided by Carpentaria Gold – Best distribution of sample points over

Ravenswood district (soil and rock chip) – Comprehensive multi-element analysis

• Data levelled for geology • Determine element associations for each

camp • Produce anomaly maps for district-scale

prospectivity mapping

Geochemistry – PCA results

• Assess element associations – Within camp – Regionally

• Limitations – Needs sufficient sample density and distribution – Interpolating between sparse points – Requires multi-element analysis for each

sample -> hence use of Carpentaria Gold data

Regional PCA (soil)

[As Au Bi Mo Pb Sb] +/- [Ag Cu Te W]

Geochemistry - zonation

• Only works in camps with sufficient data points

• Inconsistent zonation trends between camps – No characteristic zonation, each IRG camp is

different

Geology

• 1:100,000 scale mapping – Surface geology – Solid geology interpretations – Structures

• Camp scale mapping – Variable resolution: ~1:5,000 – Detailed, but patchy and limited spatial extent – Geology and structures

Geology - maps

Geophysics

• GSQ and GA – Magnetics – Gravity – Radiometrics

• Data reprocessed to create derivative products

• New interpretations – Revised structural maps – Intrusion mapping

Geophsyics maps

Intrusion detection • Automated tool in Geosoft for mapping

intrusions from aeromagnetic data

• Limitations – Resolution of data – “Circularity” – Age?

Human expert at best

Automated detection

Ground truth

Images: Geosoft; Holden et al., 2012

Intrusion detection

Statistical Correlations

• Results need to be statistically significant AND geologically meaningful to be useful for exploration

Layer Max. Contrast* % Deposits % Total Area Favourable

CG soil geochem PCA 1.6098 36% 96%

CG RC geochem PCA 1.8018 25% 92%

Dyke density 1.425 17% 6.5%

Permo-Carb intrusions No statistically significant result

* Contrast ≥ 0.5 is meaningful

• Statistically assess relationship between targeting criteria and known IRG occurrences

Prospectivity Analysis

• Integrating what worked for finding major deposits – Permo-Carb sub-volcanic intrusions

• Mapped and interpreted from geophysics – Mapped dykes – Principle component analysis using Carpentaria

Gold geochemistry data

Prospectivity map

Campoven

Three Sisters

Conclusions

• Tried lots of things that didn’t work – Data needs to be fit for purpose

• Resolution of geophysical surveys • Geochemical survey sample density and elements

analysed in the lab – This can be seen as a positive outcome

• We need to know what works, but we also need to know what doesn’t work; this is rarely discussed

Conclusions • Highlights that even with expert data

analysts working on the problem, a purely data-driven or automated approach doesn’t work here – Needs expert opinions to drive the model – We welcome input from industry

• If you want to get involved in a round-table discussion, we are tentatively planning a meeting for October/November in Townsville – Please email Arianne Ford or Vladimir Lisitsin – Arianne.Ford@jcu.edu.au or Vladimir.Lisitsin@dnrm.qld.gov.au

Acknowledgements

Geological Survey of Queensland

Resolute Mining Ltd.