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Mineral Potential Mapping for Pre-Competitive Data Delivery in NSW Zone 54

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This presentation explores the benefits of using all available geosciences data to provide the most reliable basis for exploration decision-making and from which to develop the most appropriate and cost-effective exploration programs.

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Mineral Potential Mapping for Pre-Competitive Data Delivery in NSW Zone 54

  1. 1. Mineral Potential Mapping for Pre-Competitive Data Delivery in NSW Zone 54 A. Ford, J. Fitzherbert, P. Downes, G. Partington, P. Blevin, J. Greenfield, K. Peters @Kenex-Ltd @KenexNZ @KenexLtd
  2. 2. Background • Geological Survey of NSW have an ongoing state- wide mineral potential mapping program. • Collaborative project between GSNSW and Kenex. • Results for Southern New England Orogen delivered in 2017. • Workflow developed in SNEO project uses mineral systems expertise and high-quality pre- competitive geoscience data from GSNSW. • Continuation of state-wide mineral potential mapping project into NSW Zone 54 in 2018: • Curnamona Province: BHT and IOCG • Delamerian-Thomson Orogens: Orogenic Au and VMS • Delivery of a pre-competitive geoscience data package which can be used to guide exploration and land-use planning in the region.
  3. 3. Exploration Decision Making • The context for this session: • This session explores the benefits of using all available geosciences data to provide the most reliable basis for exploration decision-making and from which to develop the most appropriate and cost-effective exploration programs. • How can we best use all of the available geoscience data that the Geological Survey of NSW provides in order to add value and assist in exploration decision making and strategic land-use planning both from an industry and government perspective? • Outcomes for the project were intended to deliver: • Pre-competitive geoscience data to assist industry with exploration decision making. • Mineral potential mapping results that can assist the government with land-use decision making, which subsequently impacts upon industry.
  4. 4. Project Workflow • Collaboration between Geological Survey of NSW and Kenex. • Workflow utilised expertise of both collaborating parties: • Compilation and review of available data (GSNSW & Kenex) • Mineral system models (GSNSW) • Spatial data table and training data selection (GSNSW & Kenex) • Making predictive maps (Kenex) • Data-driven spatial data analysis to evaluate correlations (Kenex) • Review of predictive maps and selection for mineral potential models (GSNSW & Kenex) • Making mineral potential maps and evaluating results (GSNSW & Kenex) • Reporting and delivery (GSNSW & Kenex)
  5. 5. The Available Data • Fully attributed mineral occurrence database Mineral Occurrences • Seamless basement geology • Reactive rocks • Metamorphic grade • Outcrop geology Geology • Fully attributed faults • Shear zones • Folds • Structural points Structures • Drill holes and assays • Rock samples • Soil samples • Stream sediment samples Geochemistry • Magnetics • Gravity • Magnetic & gravity worms • Radiometrics Geophysics • Point analyses with alteration and mineralogy attributes Petrology • Mineral maps derived from ASTER ASTER • DEM • Catchments Topography
  6. 6. Mineral Systems Analysis • Mineral system models can be used to determine key predictive variables for each mineral system being investigated. • Key predictive variables were grouped by mineral system process: source, transport, trap, and deposition. • Mineral system models were prepared by Geological Survey of NSW experts: • BHT Pb-Zn-Ag: GS2018/0400 • IOCG: GS2018/0371 • Orogenic Au: GS2018/372 • VMS: GS2018/370
  7. 7. Spatial Data Table • A spatial data table was developed for each mineral system in Zone 54 that lists all of the mappable targeting criteria to be considered in the spatial data analysis, the methods used to create the maps, and the results of the spatial analysis. • Documents workflows and assists with data management. Spatial Variable System Measure Source Data Technique Predictive Map Set Up Variable ID Area # TP W+ W+s W- W-s C Cs StudC Action Comments Lithology P1: Sources of Metals and Fluids Sources and host of metals and/or fluids Curnamona seamless geology Convert Curnamona geology map to raster; classify by dominant lithology (dominant_L: 1=ironstone, 2=mafic igneous, 3=siltstone, 4=tillite, 5=gneiss, 6=basalt, 7=pegmatite, 8=clastic sedimentary, 9=amphibolite, 10=sandstone, 11=dolerite, 12=metamorphic, 13=schist, 14=gabbro, 15=serpentinite, 16=metasediments, 17=granite, 18=phyllite, 19=quartzite, 20=dolomite, 21=conglomerate, 22=limestone, 23=diamictite, 24=shale, 25=graphitic schist); test for categorical spatial association litho1 WofE Method, Study Area = CurnaSA, Unit Area = 1, Training Data = BHT_TP.shp, Missing Data = -99, PP = 0.000947, confidence = 0.5 Class=5 or 7 (gneiss or pegmatite) 5525.2225 14 0.9860.2676 -1.3675 0.5774 2.3535 0.6364 3.6981Consider Potosi-type gneiss. Seamless. P1: Sources of Metals and Fluids Sources and host of metals and/or fluids Curnamona seamless geology Query Curnamona geology map for Potosi-type gneiss units (Allendale Metasediments, Broken Hill Group, Cues Formation, Hores Gneiss, Parnell Formation, Freyers Metasediments); buffer to 100km in 50m increments; test for spatial association d2potosi WofE Method, Study Area = CurnaSA, Unit Area = 1, Training Data = BHT_TP.shp, Missing Data = -99, PP = 0.000947, confidence = 0.5 0-200m 3036.0875 171.72170.2506 -2.6589 1.0051 4.3806 1.0358 4.229Consider Potosi-type gneiss. Seamless + 25k P1: Sources of Metals and Fluids Sources and host of metals and/or fluids Curnamona seamless geology; 25k surface geology Query Curnamona seamless geology map for Potosi-type gneiss units (Allendale Metasediments, Broken Hill Group, Cues Formation, Hores Gneiss, Parnell Formation, Freyers Metasediments) and 25k outcrop geology map for Potosi; buffer to 100km in 50m increments; test for spatial association d2potosi2 WofE Method, Study Area = CurnaSA, Unit Area = 1, Training Data = BHT_TP.shp, Missing Data = -99, PP = 0.000947, confidence = 0.5 0-200m 3188.675 171.67240.2506 -2.6486 1.0051 4.321 1.0358 4.1715MODEL Density of Potosi gneiss P1: Sources of Metals and Fluids Sources and host of metals and/or fluids. Proxy for stratigraphic unit volume. Curnamona seamless geology; 25k surface geology Query Curnamona seamless geology map for Potosi-type gneiss units (Allendale Metasediments, Broken Hill Group, Cues Formation, Hores Gneiss, Parnell Formation, Freyers Metasediments) and 25k outcrop geology map for Potosi; convert to raster classifying by polygon area; reclassify into 10 classes using natural breaks; test for spatial association potosi_dens WofE Method, Study Area = CurnaSA, Unit Area = 1, Training Data = BHT_TP.shp, Missing Data = -99, PP = 0.000947, confidence = 0.5 High density (Class 10-2) 1935.555 71.34290.3786 -0.4169 0.3163 1.7598 0.4934 3.5666Do nothing Insufficient capture of training points
  8. 8. Mineral Potential Mapping • Weights of evidence (WofE) approach was used: • Statistically-driven, but user retains control over model. • Quantifies spatial association between training points (known mineral occurrences) and predictive maps (e.g. distance to mafic gneiss, fault density). • Training data selected by GSNSW experts for each mineral system. • Predictive maps generated and WofE used to quantify spatial association with training points for each mineral system. • Selection of predictive maps for inclusion in mineral potential maps and running models for each mineral system: • Maps need to be statistically valid, geologically meaningful, and practically useful. • Testing efficiency of classification for each mineral potential map produced.
  9. 9. Training Data – Curnamona Models 17 BHT 3 IOCG • IOCG model has insufficient training points for a WofE analysis • Modified approach used. • Statistical values were assigned from equivalent predictive maps generated in a previous Kenex Mt Isa-Cloncurry IOCG study.
  10. 10. Key Exploration Criteria – Curnamona Models Data Type (Mineral System Component) Key Variables (BHT) – 85 maps evaluated Key Variables (IOCG) – 97 maps evaluated Lithology/Stratigraphy (Source/Transport/Trap) Pre-Olarian Orogeny geology: Potosi-type gneiss; pegmatites; rift-sag phase boundary; thin exhalite units (quartz-gahnite); mafic units; linear stratigraphic units; competency contrast. Post-1580 Ma geology: gneiss, schist, mafic intrusions; pegmatites; ironstones; Cues Formation, Himalaya Formation, Rantyga Group, Curnamona Group. Mineral occurrences (Source/Deposition) Known occurrences of Pb, Zn, and Ag. Proximity to Cu and/or Au occurrences. Known occurrences of Cu and Co. Petrology (Source/Transport/Trap) Blue quartz. Biotite-magnetite; actinolite-biotite-epidote; or quartz- chlorite-sericite alteration. Faults/Folds (Transport/Trap) Fault sub-sets including: NE trending faults; point datasets derived from the fault dataset, particularly fault intersections and bends; folds. Faults active pre-mineralisation; shear zones. Magnetics/Gravity (Source/Trap/Deposition) Magnetic RTP low; gravity high; gravity 1VD high. Spatially coincident magnetic and gravity highs. Magnetic/Gravity worms (Transport) Magnetic worms with heights of 1200 and 65188. Gravity worms with heights of 1080 and 4842. More value could potentially be extracted from the worm data with further processing. Magnetic and gravity worms that are indicative of mid- crustal structures. More value could potentially be extracted from the worm data with further processing. Radiometrics (Deposition/Source) U, Th, and K radiometric highs. U, Th, and K radiometric highs. Stream sediment geochemistry (Deposition) Stream catchments containing stream sediment samples with anomalous Pb. Stream catchments containing stream sediment samples with anomalous Cu and Co. Rock chip and drill hole geochemistry (Deposition) Rock chip and drill hole assays with anomalous Ag, Pb, Zn, Cu, and Au. Rock chip and drill hole assays with anomalous Cu and Co.
  11. 11. Selecting Predictive Maps • Predictive maps need to be geologically meaningful, statistically valid, and practically useful. • Understanding what the mineral potential maps are going to be used for: exploration targeting and land-use planning have different requirements. • Maps with the best statistics might not be fit for purpose. • Ensuring there is at least one useful map to represent each mineral system process for each mineral system being modelled. D2Potosi D2RiftSag PbZnAg
  12. 12. Broken Hill Type Pb-Zn-Ag Mineral Potential • Prospective area defined as having a post probability higher than prior probability. • Prospective area = 8.85% of study area • Highly prospective area = 2.3% • Efficiency of classification = 99.1% • All training points are in prospective area. • 15 of 17 training points fall in highly prospective area (Pinnacles and Flying Doctor are in prospective area).
  13. 13. IOCG Mineral Potential • Due to insufficient training data, prior probability and efficiency of classification could not be calculated. • Post probability map reviewed and prospective area thresholds defined manually. • Prospective area = 5% of study area • Highly prospective area = 1% • Predicts location of known IOCG occurrences at Copper Blow, Copper King, and Son of Man.
  14. 14. Project Outcomes • High quality pre-competitive geoscience data. • Comprehensive and robust mineral system models for NSW Zone 54. • Detailed spatial data analysis to test mineral system models. • Existing and new ideas either confirmed or to be re-evaluated. • Interesting research questions raised about why the spatial data analysis produced a few unexpected results. • Acknowledged limitation biasing the results to areas of outcrop • Limited data available for selected model layers outside of outcropping areas • Mineral potential maps which synthesise complex data and mineral system understanding for BHT Pb-Zn-Ag, IOCG, Orogenic Au, and VMS in the Curnamona Province and Delamerian-Thomson Orogens. • NSW Zone 54 Mineral System Atlas to be released to the public which includes: training points, predictive maps, weights tables, mineral potential maps, spatial data tables, and report. • Allows end-users to combine their own selection of predictive maps to produce their own mineral potential maps.
  15. 15. Our Business Is To Help Companies Discover New Opportunities www.kenex.com.au@Kenex-Ltd @KenexNZ @KenexLtd

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