PLATE 16: Polymictic breccia composed mostly of rhyolite angular clasts cemented by cryptocrystalline quartz. Cut by later fine veinlets of clear quartz. Hydrothermal breccia from Sherwood, Agate Creek, Forsayth (Sample #231516; +100 g/t Au, 42 g/t Ag).

METALLOGENIC STUDY OF THE GEORGETOWN, FORSAYTH AND GILBERTON REGIONS-Part 4

August 19, 2019

PLATE 23: Sample from the Caledonia workings, Gilberton. Shows brecciated early, white, tightly packed, euhedral buck quartz (Plutonic style) cemented by clear, comb textured quartz, colloform banded and in places moss textured chalcedonic silica and siderite (Epithermal style).

METALLOGENIC STUDY OF THE GEORGETOWN, FORSAYTH AND GILBERTON REGIONS, Part 6

August 19, 2019

METALLOGENIC STUDY OF THE GEORGETOWN, FORSAYTH AND GILBERTON REGIONS, Part 5

Published by Terra Admin at August 19, 2019

8.0 MULTI-ELEMENT GEOCHEMISTRY

Multi-element geochemical data from 203 prospects in 54 camps for the Georgetown region have been interpreted to classify the hydrothermal systems and to establish internal geochemical zoning patterns for all the main represented classes of hydrothermal deposits.

The data was sourced from the most recent geochemical database for the region held by the GSQ and derived mainly from the compilations by TerraSearch (pre-2014) and Map-to-Mine (2016). This has been augmented with company supplied confidential data from a number of major projects (Agate Creek, Gilberton, Kidston, Woolgar) and from numerous prospects with additional data or a more comprehensive suite of elements. The interpretation of this data is supplied as part of the project, but the raw data is not.

In this project rock chip and drill-hole data have been used in preference to soil and stream data to establish true templates of the hydrothermal systems and zoning models without the complexities of differential transport of elements by surficial processes. Diagnostic metal associations and zoning patterns can be recognised in residual soils and the stream sediments can be useful guides to system location using pathfinder elements. Some of the case histories presented here include interpretation of this data.

While much of the modern data analysed using ICP methods include up to 51 elements that can be used for rock type and alteration classification, the suite used here is for the metallic elements that are commonly concentrated in magmatic-hydrothermal systems. The overall classification scheme is based on a 12-element suite (Au-Ag, As-Sb, Cu-Pb-Zn, Bi-Te, Mo-W-Sn ± Ba, Hg, Mn and Se) that has proven useful for both deposit classification and geochemical zoning. The classification is determined from the relative enrichment of the elements, estimated as the average element concentration in a sample suite divided by the corresponding average concentration values for the dominant host rock (Tables 8.1 & 8.2). The elements are then listed in the order of relative enrichment and tabulated for comparison with the existing general classification scheme for north Queensland (Tables 8.3, 8.4 & 8.5) which tags the major hydrothermal system classes and alteration zones by geochemical association and inferred spatial proximity to a causative intrusion.

Table 8.1: Average crustal abundance of selected metallic elements typical of felsic magmatic-hydrothermal systems (selected from Levinson, 1974)

Table 8.2: A typical interpretation using the methodology outlined above based on a dataset of rock chip samples where n=238, there is no data for Ba Sn W and all the values are in ppm.

8.1 METHODOLOGY OF MULTI-ELEMENT GEOCHEMISTRY INTERPRETATION

The methodology that has been adopted for multi-metal data interpretation is as follows:

Extract data for the selected 12-16 elements used for system classification and geochemical zonation interpretation from a prospect geochemical database.
For each element, sort the data and replace the results below detection limit by average crustal abundance for the appropriate host rock type. This is generally granite for Georgetown region.
Define populations by prospect within the camp and for the camp overall and calculate an average of the raw values for each population.
Normalise the average for each population by host rock type (e.g., granite) to obtain the relative enrichment of each element for the population (Table 8.1).
List relative enrichments in orders of magnitude signifying enrichment >1000 bold; 100-1000 normal and 100-10 in italics.
Compare with the general metal zoning chart used to define system class (Table 8.5).

Working through an example below shows how the interpretations were typically made (Table 8.2):

The average value for each element analysed (column #2) is divided by background value for the particular lithology involved, in this case granodiorite (column #3). The result is the enrichment factor (column #4).
The enrichment in this example is: Au Ag Pb Bi As Cu Te Zn, where the elements are listed in the order of decreasing enrichment.
In this example, Au is associated with strongly elevated Ag, Pb, Bi, which indicates a magmatic system with a moderate fractionation (Bi>Te). Strong fractionation would have higher base metals enrichment.
Bi ± Te rich suggests an intermediate intrusive source, like granodiorite.
Ag-Pb-As imply a distal zone is being sampled at surface, based on the general zoning model (Table 8.5).
Overall, this sample suite could represent a relatively mafic magmatic hydrothermal system emplaced at a deep porphyry level. This is consistent with known geology and presence of euhedral buck quartz veins and granitic vein dikes in this occurrence.

8.2 INTERPRETATION OF GEORGETOWN MULTI-ELEMENT GEOCHEMICAL DATA

The multi-element data interpretation for 56 systems has been tabulated (Table 8.3) and a classification scheme built on the element class has been established (Table 8.4). The aim was to distinguish the different genetic clans of mineralisation and establish the level of current exposure in the zoned systems.

In the Georgetown region, the main interest is in the geochemical distinction between what have been called Early Devonian granite-hosted plutonic lodes like those in Georgetown and Forsayth districts; the early Carboniferous IRGS like Kidston; the early Permian IRGS systems like Phyllis May; and the early Permian epithermal systems like Agate Creek.

The geochemical classification scheme was built from the enrichment suite by designating some key element ratios (Table 8.4). In the code list the Au/Ag ratio, the Bi/Te ratio and the position of As-Sb versus Pb-Zn-Cu are mainly diagnostic of the system type, whereas the relative position in the enrichment table of As Sb Cu Pb Zn Mo W are indicative of the level (metal zone) in the system.

High Te and Bi are key indicators of magmatic-hydrothermal systems overall and also vary systematically, with Te dominant in mafic systems and Bi dominant in felsic systems. The Bi/Te ratio thus progressively increases going from mafic to felsic intrusion-related systems (Table 8.5). Plutonic and Epithermal systems have much lower or absent Bi and Te so they are mid-range or weak in the enrichment list. As-Sb are more consistently high in the enrichment list than base metals for plutonic and epithermal systems whereas base metals are more prominent in IRGS.

The CHEM classification (Table 8.3) is determined by listing the three dominant elements from the enrichment suite. The level in the system or ZONE (Table 8.3) is labelled using the style of mineralisation (Plutonic = P, IRGS = I, Epithermal = E) and a code representing the important metal ratios from Table 8.4. The CLASS code is a generalisation of the CHEM codes based on Au – Ag ratios and Te-Bi levels (Table 8.3 & 8.4).

Table 8.3: Classification of camps in the Georgetown region based on interpretation of enrichment ratios of hydrothermal metallic elements.

The CLASS scheme has three main groups and a minor group that represent fundamentally different genetic systems of mineralisation (Figure 10):

The TB group has Te & Bi as the dominant elements in the enrichment suite more or less independent of what else is in the enrichment suite and independent of Te/Bi ratio. This is the predominant group in the Georgetown region and covers the majority of IRGS, many of the plutonic camps and even some of the epithermal deposits (Table 8.3). The Te-Bi class is generally interpreted as of magmatic origin and is typical of IRGS elsewhere in north Queensland (Morrison 2014, Morrison et al 2016). The implication for Georgetown is that not only the IRGS, but the epithermal deposits and most of the plutonic deposits that don’t have mappable related intrusions still have a magmatic fluid contribution.
The SAT group has Au>Ag, As-Sb prominent and Te prominent but without Bi. This is a common group in Georgetown region and represents the distal parts of some of the IRGS and plutonic deposits. The Au-As-(Te) association is generally interpreted as typical of epithermal and orogenic gold deposits especially where the Te is relatively subdued (e.g. Hodgkinson Province deposits-Morrison & Lisitsin, 2017). The implication for Georgetown is that there is no ready distinction between IRGS plutonic and possible orogenic deposits based on the geochemical model alone.
The GST group has Ag>Au; As-Sb prominent and Te-Bi universally present but not prominent. This chemistry uniquely represents the group of Permian Intrusion-Related Deposits in the area NW-W & SW of Georgetown. This group is not generally recognised elsewhere but its characteristics fit Intrusion-Related Mineral Systems with Ag dominant over gold.
The ASM group has Au>Ag, prominent basemetals (Pb, Zn, Cu) and common As-Sb without Bi, Te. This is typical of pluton-hosted orogenic deposits such as Charters Towers.

Table 8.4: Code set of element ratios used in the classification scheme and CHEM code groups sorted into CLASS.

Figure 10 : Camps coloured by Geochemical Class labelled with the metal zone for each camp.

The classes within the IRGS clan relate to level of emplacement and/or the composition of the related intrusion, so there is a distinction of Bi-rich felsic intrusive systems from Te-rich mafic intrusive systems, and those more enriched in As-Sb compared with base metals in the plutonic IRGS group (Table 8.5).

8.3 GEOCHEMICAL ZONATION PATTERNS

The geochemical zoning template (Table 8.5) was constructed during a major project on eastern Australia Intrusion-Related Mineral Systems (IRMS) undertaken by Gregg Morrison and Phil Blevin in 1994-1997 as AMIRA Project P425. The template identifies variations in metal zoning patterns for IRMS emplaced mainly at the porphyry level and related to intrusions of different magmatic geochemistry and evolution. It is based on more than one hundred examples from north Queensland which were then compared with international examples considered typical of the magma chemistry spectrum. A feature of Table 8.5 is the consistent overall pattern of metal zones that allows the classification to include five standard metal zones that can be used in place of the local metal suite. The table also allows the subtle differences between system types to be identified with key elements and element assemblages. For example, the position of maximum gold concentration in relation to a causative intrusion varies systematically from nearest to the intrusion (the core) in mafic systems to progressively more distal in felsic systems. This is shown by the shading in Table 8.5 and illustrated by Figure 11. In addition, Bi and Te that are demonstrated consistently as key indicators of magmatic-hydrothermal systems also vary systematically, with Te dominant in mafic systems and Bi dominant in felsic systems. The Bi/Te ratio thus progressively increases going from mafic to felsic systems.

In the Georgetown region, the majority of IRMS fall in one of three groups characterised by Cu-Au, Cu-Mo or Mo-W-Bi metal association in the core element zone (Table 8.3). However, almost all of them have been targeted for gold, rather than base metals as the economic commodity, so they fall into the more specific category of intrusion related gold systems (IRGS). This makes the zoning models valuable because the Au-rich part of the systems may not be exposed at surface, which, if misunderstood could have discouraged historical gold exploration. As a consequence, our metallogenic work has targeted identification of IRMS systems and the geochemical data search has tried to classify and develop zoning models for these systems, so they can be ranked in terms of likely position of the best gold in relation to current surface exposures.