surficial uranium targeting model and prospectivity mapping of the yeelirrie area, western australia...

Surficial uranium targeting model and prospectivity mapping of the Yeelirrie Area, Western Australia

Abstract: An exploration targeting model for calcrete-hosted surficial uranium deposits in the palaeochannels of Western Australia is presented. The model is used to develop a knowledge-driven Mamdani-type fuzzy inference system for prospectivity modelling of calcrete-hosted surificial uranium deposits the Yeelirrie area. The inputs to the model are. spatial proxies that serve as expressions of mineralization processes in the publicly available exploration datasets. In the output prospectivity map, the SE-NW trending Yeelirrie and E-W trending Hinkler's Well palaeochannels show high prospectivity. The known surifical deposits fall in high prospectivity areas, although the minor showings and anomalies in the southern part of the study area fall in low prospectivity areas. A comparison with the available radiometric images shows that several channels showing high surface U concentrations in the NW and NE quadrants may not be prospective.

Objectives• Develop exploration targeting model of calcrete-hosted surficial uranium deposits in

palaeochannels of Western Australia;,• Generate prospectivity model of calcrete-hosted surficial uranium deposits in the

palaeochannels around Yeelirrie, Western Australia,

Methodology

U-Sources Genetic and Targeting Model

Targetting Criteria Possible Predictor maps or spatial proxies Predictor Maps Generated- Used-Not Used Rationale

1. U-Content- Considerably high in continental crustal rocks.

- As catchment area of a palaeochannel increases, fluids collect more U from larger areas.

Proximity to continental crustal rocks

Radiometric data indicating U enrichment

Geochemical data on U enrichment in ground waters

Size of catchment area of palaeochannel

Give Map of Underlined

2. Source rock Geochemistry

- Type of rock affecting its suitability as a leachable source of U

Proximity to -peraluminous granites, -peralkaline felsic volcanic rocks, -pre-existing U mineralization,

Mineralogy: minerals with high U contents and also susceptible to weathering: biotite, monazite, zircon, apatite, allanite; biotite favourable as a leachable source for U

Data not available

3. Source rock structure

- To determine the degree of weathering that facilitates extraction of U from sources by the circulating fluids.

Structural data: density of fractures, faults, joints

Calculation of weathering indices from geochemical data

Calculation of Fluid-rock ratio

Mapping geomorphology: granitic topographic highs more susceptible to weathering

Meteorological data for temperature fluctuation (weathering due to temperature fluctuation )

4. U enrichment in ground water

- Near-surface oxidizing environment for leaching U

Hydrological data to calculate the Eh-pH of fluids for leaching U;

Oxidation state of fluids and pH of waters (acidic or alkaline waters)

Data not available

Constituent Processes

Targeting Critera Possible Predictor Maps/Spatial Proxies

V Sources 1. Presence of mafic-ultramafic rocks

2. Redox environment: Extraction of V from source rocks by acidic fluids occurs in reducing fluid Eh. If the same fluid contains uranyl complexes, then strong reducing Eh can cause destabilization of uranyl complexes. Hence such fluids should be mildly reducing.

Proximity to greenstone belts, mafic-ultramafic rocks, BIFs

Eh-pH of fluids

Mapping the depth of water table

K Sources Continental crustal rocks Proximity to Continental crustal rocks; Ground water geochemistry gives dissolved K+ ion content

Energy for driving fluids

Hydraulic gradient for subsurface fluid circulation from source to trap

Mapping topography and slopes of palaeovalley floors (using DEM or thermal infrared data); Palaeochannel mapping- Flow directions from slope calculations and palaeochannel intersections; Caution: Topographic gradients may not be exact proxies for hydraulic gradients

1

Conceptual Model & Knowledge

Filtering & Analysis

Mineral systemsModels

Data Compilation & PreparationLiterature

Review

Spatial Analysis

Predictor MapsNeural Networks

(NN)

Fuzzy Inference Systems (FIS)

Weights of Evidence (WofE)

Prospectivity Modelling