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

Transcription

1 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 2

3 Aims and Scope Evaluate available data Develop targeting criteria for intrusionrelated mineral systems Specific focus on geochemistry Generate prospectivity maps Develop a method for ranking existing targets

4 Study Area

5 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

6 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

7 Data Geochemistry Geology Geophysics

8 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

9 Geochemistry maps

10 Carpentaria Gold Geochem

11 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

12 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

13 Regional PCA (soil) [As Au Bi Mo Pb Sb] +/- [Ag Cu Te W]

14 Geochemistry - zonation Only works in camps with sufficient data points Inconsistent zonation trends between camps No characteristic zonation, each IRG camp is different

15 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

16 Geology - maps

17 Geophysics GSQ and GA Magnetics Gravity Radiometrics Data reprocessed to create derivative products New interpretations Revised structural maps Intrusion mapping

18 Geophsyics maps

19 Intrusion detection Automated tool in Geosoft for mapping intrusions from aeromagnetic data Human expert at best Automated detection Ground truth Limitations Resolution of data Circularity Age? Images: Geosoft; Holden et al., 2012

20 Intrusion detection

21 Statistical Correlations Statistically assess relationship between targeting criteria and known IRG occurrences Layer Max. Contrast* % Deposits % Total Area Favourable CG soil geochem PCA % 96% CG RC geochem PCA % 92% Dyke density % 6.5% Permo-Carb intrusions * Contrast 0.5 is meaningful No statistically significant result Results need to be statistically significant AND geologically meaningful to be useful for exploration

22 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

23 Prospectivity map Campoven Three Sisters

24 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

25 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 Arianne Ford or Vladimir Lisitsin Arianne.Ford@jcu.edu.au or Vladimir.Lisitsin@dnrm.qld.gov.au

26 Acknowledgements Geological Survey of Queensland Resolute Mining Ltd.