Mapping gold pathfinder metal ratios – A methodological approach based on

compositional analysis of spatially distributed multivariate data

Braga,L.P.1, Porto,C.1, Silva,F.2 and Borba,R.1

1Federal University of Rio de Janeiro

2Federal Rural University of Rio de Janeiro Rio de Janeiro, RJ, Brazil

Geochemical exploration in deeply weathered terrains is often hampered by a strong modification of primary geochemical signals at surface, making it difficult to locate potential targets using geochemical results of soil samples.

This problem is particularly relevant in the Amapari region, Amapá state, Brazil, where 10,235 soil samples were collected in an extensive grid covering ~ 500 km2, whose samples were analysed by ICP-MS after an Aqua Regia digest producing results for 51 elements.

The methodology applied in this work is directed at mapping potential targets for Au mineralization using compositional kriging of ratios of Au pathfinder metals

The samples were classified according to distinct groups: training, composed of mineralized samples over a known deposit area (B) and non-mineralized samples from a background area (A); validation, composed of another set of mineralized samples over the same known deposit area (B) and non-mineralized samples from the same background area (A)

Training will be used to generate a model and Validation to evaluate its performance with samples not used in the modelling steps. The criteria to validate the model uses the area location (A or B) where the sample falls and its metal content

B

A

From the 51 initial elements, (Au) was eliminated and 19 others as well as like B, Ba, Ca,Cd, Ce, Co, Mg, Mn, Na, P, Re, Sb, Se, Sn, Sr, Ta, Te, Y, Zn based on exploratory analysis. For example, Boron (B) has almost every sample below detection limit (bdl). Another example is phosphorus (P) which was excluded for showing similar behaviour in both areas A and B.

In addition to pure statistical reasons, geological analysis was taken into account, eliminating five more metals – (Be, Cs, K, Li and Rb) because they were associated to granitoids and not to gold.

DrainageBif= bandded iron formationGn= gneissGr= grantieGrdt= granodiorite-----UM= ultramafic rocksVNO= Vila Nova metavolcanic sequence

The Compositional Analysis approach was adopted due to the nature of the data. Data was treated with "compositions", an R-package for the analysis of compositional and positive data using different approaches, written by K. Gerald Van den Boogart, Raimon Tolosana-Delgado and Matevz Bren (version 1.40-1).

In the training phase of the model metals associated with the Au mineralization were identified using Compositional Principal Components.

Based on these plots a Compositional Discriminant Function was developed to identify the origin of each sample (A or B) for the selected metals. .

The second set, validation, is composed of mineralized samples over the same known deposit area (B) and non-mineralized samples from the same background area (A). A satisfactory test with the same discriminant function obtained from the training sample was performed.

The confusion matrix shown below contains information about actual and predicted classifications done by the Compositional Discriminant Function. It allows it to evaluate the accuracy of the classification done.

Actual

Predicted

False Negative

False Positive

The third set to be tested (C), prospection, is composed of samples from the rest of the area covered by the grid but where Au mineralization has not been recognized. However, the results were not meaningful and another approach was assumed.

C

A compositional kriging interpolation was used to build a map with the prospection samples where only geochemical results were available. The first step was to obtain models of Compositional Crossed Variogram with the metals selected in the previous phase. Below the adjusted model for the As-Hf-Tl subcomposition.

As compositional kriging cant be calculated directly on the subcompostions, several ratios(balances) between metals were tried before the balance Tl/Hf was chosen for the trainning area (A+B). Certainly, the result is not categorically represented as in the discriminant function approach. The value is a signal that points to areas more or less mineralized (green: poor mineralization up to white: high mineralization).

B

A

BA

The adjusted variogram model (A+B) was then applied to the kriging of the third sample (C). The balloons (left) and the stars (right) point to effective minning activity.

This result is geologically and experimentally coherent. First, the distributions of Hf and Tl in the region show that area A has much more Hf than area B and that Tl is present in area B but not in A. This behaviour was observed after the present balance kriging map was produced.

Second, a mining company has been working in this area and is effectively mining gold in the areas inside the signal map obtained from the balance Tl/Hf.

Trainning A+BProspection C

(without A+B data)

Summing upWe propose a methodology for mapping gold pathfinder metal ratios as a tool to reinforce geochemical signals. The basic steps are:1. Exploratory analysis of the data2. Calculation of compositions3. Principal Component Analysis4. Selection of subcompositions5. Modeling the logratio variogram(trainning)6. Selection of Balances(trainning and validation)7. Mapping the interpolation of composition represented by a chosen balance(prospection)

References

Pawowlsky,-Glahn, V., Egozcue, J.J., Tolosana-Delgado,R. (2015)Modelling and analysis of compositional data: Wiley

Boogaart, K.G. Tolosana-Delgado, R. (2013) Analyzing Compositional Data with R: Springer

Melo, L.V. de, Villas, R.N., Soares, J.S., Faraco, M.T.L. (2003) Geological setting and mineralizing fluids of the Amapari gold deposit, Amapá State, Brazil: BRGM-France3.