lacassie et al 2011 geomin article reduced

8/6/2019 Lacassie Et Al 2011 Geomin Article Reduced

http://slidepdf.com/reader/full/lacassie-et-al-2011-geomin-article-reduced 1/10

Geochemical patterns of active stream sediments:An artificial neural networks approach

Juan Lacassie

Servicio Nacional de Geología y Minería, SERNAGEOMIN, Chile

Javier Ruiz‐Del Solar

Advanced Mining Technology Centre, Universidad de Chile

Alejandro Díaz

Empresa Nacional de Minería, ENAMI, Chile

Leonardo Baeza and Felipe Astudillo

Department of Geology, Universidad de Chile

ABSTRACT

A new methodology has been applied for the study of four impacted and non‐impacted mine‐

related fluvial systems in central and northern Chile: the Rapel, Lluta, Huasco and Limarí fluvial

systems. The method involves chemical compositional data of the < 180 μm fraction of active stream

sediments samples collected along these fluvial systems.

The chemical data has been analysed as a whole by using an advanced statistical technique based

on dynamic growing self organising maps. This technique enables the visualisation of major and

secondary chemical patterns along specific parts of these fluvial systems.

The results show that, for each fluvial system, the data can be separated into a limited number of groups

with distinguishing geochemical characteristics, which are restricted to specific segments of the rivers.

In particular, this technique enables the recognition of, in the first place, metal contamination

(Cu, Mo, Sb, Co, As and Zn) along the Cachapoal river, a highly populated area of the Rapel

fluvial system, which could be due to downstream remobilisation of mining ‐derived material

from the El Teniente Cu‐Mo mine; and in the second place, recognition of specific chemical

signatures associated with prospective and/or mineralised areas in the Lluta, Huasco and

Limarí fluvial systems.

The results show that this method presents a high potential for: first, the assessment of possible

mine‐associated river contamination and second, the identification of prospective areas and their

characteristic geochemical signatures.

Moreover, this technique uses the linear and non‐linear information contained in the chemical data.Therefore, it highlights higher order correlations and allows visual perception of subtle inter‐

relations between the chemical variables, which may have not been previously recognised by other

conventional linear statistical techniques.

– 1 –



INTRODUCTION

The chemical composition of stream sediments have been used extensively to monitor river quality, to

determine the influence of bedrock geology and to assess the impact on the fluvial systems of

anthropogenic factors such as mining activities, industry and urbanisation. The study of stream

sediments is an issue of environmental and human health concern, especially since sediments are asink for environmental contaminants and may function as a source of heavy‐metal exposure to

aquatic organisms. In turn, geochemical surveys based on analysis of stream sediments, has shown to

be a robust method for identifying areas of high mineral potential. Concordantly, during the last five

decades, worldwide systematic collection of stream sediment has been carried out [1] and the volume

of the associated geochemical grows continuously. The manner in which these data are interpreted

and visualised provides competitive advantages and exploration opportunities [2]. Therefore

advanced multivariate statistical procedures are required. In particular, Self‐Organising Maps (SOM)

[3] provides a non‐linear, non‐parametric, rapid and robust tool for analysing multivariate data.

In this contribution we use Growing Cell Structures (GCS) [4], an extension of SOM, both for

clustering fluvial geochemical data and for finding visual representations of geochemical data that

simplify the analysis and yet allow all relevant information to be preserved. The method has beentested with geochemical analyses of active stream sediments collected from the Lluta, Limarí,

Huasco and Rapel fluvial systems, in Chile. These fluvial systems, respectively supply water to

catchments with relatively large populations, whose main economic activities are agriculture and

mining. In particular, in the upper reaches of the Rapel catchment, is located the El Teniente

porphyry Cu‐Mo deposit, the worldʹs largest underground copper mine (Figure 1). In turn, in the

upper reaches of the Huasco catchment, is located the Pascua‐Lama mining project, a large gold

open‐pit mine which is planned to start operating in the coming future (Figure 2).

METHODOLOGY

Sampling, sample treatment and chemical analysis

Respectively, a total of 109, 126, 131 and 90 stream sediment samples were collected along the Lluta,

Limarí, Huasco and Rapel fluvial systems. Each sample corresponds to a composite of stream

sediment sub‐samples, taken over a channel length of 20‐50 m, collected using a plastic scoop, and

then combined and stored as single samples in plastic bags. At the laboratory of Sernageomin, the

samples were oven‐dried (60°C for 48h), cooled to room temperature (25°C), and subsequently

sieved using stainless steel mesh sieves in order to separate the fraction finer than 180 μm (< 180 μm

fraction) which was grounded in an automatic agate mill. The element concentration of the grounded

material was determined at ACME Labs with an ICP‐AES (Lluta and Limarí samples) and at

Sernageomin, with an ICP‐AES (Rapel samples) and with an X‐Ray Fluorescence Spectrometer

(Huasco samples). Additionally, the Hg and Au concentration of the Huasco samples weredetermined at Sernageomin by Atomic Absorption Spectrometry.

Geochemical datasets

The four geochemical datasets (Lluta, Limarí, Huasco and Rapel datasets) comprise the

geochemical data of the < 180 μm fraction of the collected stream sediment samples. Each sample

includes information concerning the concentrations of major oxides and trace elements (Table 1). In

– 2 –



order to examine the influence of the geographical variation of the bedrock in the geochemical

composition of the sediment samples, the East‐UTM coordinate of each sample was also included

as an extra input variable.

Table 1 List of chemical elements included in the geochemical datasets

Dataset Major Oxides

(wt%)

Trace elements

(ppm)

Trace elements

(ppb)

Lluta SiO2 , TiO2 , Al2O3 , K2O, Ba, Be, Co, Cs, Ga, Hf, Nb, Rb, Sc, Sn, Sr, Ta, Th, Au.

and

Limarí

Na2O, CaO, Fe2O3 , MgO,

MnO, Cr2O3 , P2O5.

U, V, W, Zr, Y, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb,

Dy, Ho, Er, Tm, Yb, Lu, Mo, Cu, Pb, Zn, Ni, As,

Cd, Sb, Bi, Ag, Hg, Tl, Se.

Huasco SiO2 , TiO2 , Al2O3 , K2O,

Na2O, CaO, Fe2O3 , MgO,

MnO, P2O5.

Ag, As, Ba, Bi, Ce, Co, Cr, Cu, Hg, La, Ni, Pb,

Sb, Sr, V, Y, Zn, Zr.

Au.

Rapel SiO2 , TiO2 , Al2O3 , K2O,CaO, Fe2O3 , MgO, MnO,

P2O5.

As, B, Ba, Ce, Co, Cr, Cu, La, Mo, Ni, Pb, Sb, Sr,V, Y, Zn.

‐

Multi‐dimensional analysis

Prior to the multi‐dimensional analysis, each dataset was standardised, so that the mean and variance

of each variable equals to 0 and 1, respectively. Each dataset was analysed separately using the GCS

algorithm. Starting from a three unit GCS map, the coupled GCS learning and network growing

process continued until the insertion of a new unit, do not involved relevant geochemical

discrimination between the units of the GCS map. After the GCS map size is selected, in this case

using expert criteria, the average values for individual input variables are then displayed on colourmaps and overlain on the final GCS map structure, allowing simple visual comparisons.

RESULTS

For each dataset, the GCS analysis of resulted in a neural map composed of interconnected nodes,

each node representing a group of sediment samples with similar geochemical characteristics (Figures

1 to 4). Each node groups samples with similar geochemical features that are broadly restricted to

specific segments of the corresponding fluvial system. In each fluvial system, the resulting

geographical distribution of the sample‐node associations shows that there is a close relationship

between the geographical position and the geochemical signature of the studied stream sediments.

Rapel fluvial system

The GCS analysis of the Rapel dataset resulted in a neural map composed of eight interconnected

nodes, each associated with a group of sediment samples that presents similar geochemical

characteristics (Figure 1). The geographical distribution of the sample‐node associations shows that,

in the upper part of the catchment, it is possible to distinguish three geochemical segments (Figure 1).

In particular, the upper to middle reaches of the Cachapoal river are characterised by the 2‐8 node

– 3 –



association. Thus the corresponding stream sediments present typical high concentrations of Cu, B,

Mo, Sb, Co, As, Zn and CaO (Figure 1). This chemical signature of elevated Cu‐Mo‐As‐Sb

concentrations is typical of mine affected fluvial systems. Thus, it likely reflects downstream

remobilisation of material associated with the El Teniente Cu‐Mo mine. Downstream, the sediments

of the lower reaches of the Cachapoal river, are almost completely associated with node 6 (Figure 1).

This chemical change occurs broadly downstream the confluence with the Claro river. Thus, it

reflects a dilution effect, as indicated by the lower Cu, B, Mo, Sb, Co, As, Zn, Pb and CaO

concentrations of the associated sediments. In the southern part of the catchment, the Tinguiririca

river is dominated by node 1, which is characterised by conspicuously high P2O5 , Ba and Sr

concentrations (Figure 1). In particular, the high P2O5 concentrations reflect an extensive use of

phosphates in the floodplain of this river.

– 4 –



Figure 1 (a) Sample‐node distribution in the upper catchment of the Rapel fluvial system (modified from [5]).

Insert shows the GCS neural Map. Only the nodes relevant for this part of the catchment are coloured. The

samples present the same colour of the node to which they are associated. Their sample ‐number (sample‐code)

is also indicated. Geology modified from the Geological map of Chile [6]. ET: El Teniente Cu ‐Mo mine (white

star symbol). TD1‐3: main tailing dams. LL: Los Leones creek. Crc: Claro river creek. (b) Visualization of the

distributions of each input variable. Scales at right indicate whole rock concentrations (wt%) for the major

oxides and parts per million (ppm) for the trace elements.

– 5 –



Huasco fluvial system

The analysis of the Huasco dataset resulted in a neural map composed of nine interconnected nodes

(Figure. 2). The geographical distribution of the sample‐node associations shows that, in

geochemical terms, it is possible to distinguish different segments along this fluvial system (Figure. 2).

In the upper part of the catchment, the Estrecho and Chollay rivers are respectively characterised by node 1‐ and node 9‐ associated samples. This indicates that: 1) the Estrecho river presents a

conspicuous geochemical signature of high concentrations of Au, Hg, As, Sb, Pb, Ba, La, Ce, SiO2 ,

K2O and Sr; 2) the same signature, although diluted, characterises the Chollay river (Figure. 2). In

particular, the Au‐Hg‐As‐Sb‐Pb‐Ba anomaly of the Estrecho and, to a lesser extent, of the Chollay

river, fits well with the mineralisation and alteration paragenesis described for the Pacua ‐Lama

deposit. This includes native gold, tellurides, calomel (Hg2Cl2), arsenolite, enargite, stibnite, galena,

anglesite and barite [7]. Therefore, the geochemical signature of the sediments of Estrecho and

Chollay rivers, corresponds to a natural signature that reflects the presence of an important

mineralised area in the upper reaches of their catchment. This area corresponds to the alteration

zone where the Pascua‐Lama mining project is located (Figure. 2). In turn, the Del Tránsito river,

located downstream, is dominated by a node 6‐4 association (Figure 2). In particular, thegeographic distribution of the node 6‐associated samples, which present typical high values of Cu,

Ni and Zn, broadly coincides with restricted outcrops of Palaeozoic basement (Figure 2). This

suggests that these outcrops are potential exploration targets for Cu‐Ni‐Zn mineralisation.

The node 3‐association also characterises the upper reaches of this catchment. It is typicall of the

Potrerillo, Tres quebradas, Chollay and Conay rivers. The associated samples present high

concentrations of SiO2 , K2O, Sr and La (Figure 2). This can be interpreted as a background chemical

signature that reflects detritus from sources with hydrothermal alteration located in those areas [8].

In this context, both the chemistry and geographic distribution of the node‐8 associated samples

suggest that they reflect a dilution of the node‐3 signature downstream the Del Carmen river (node 8).

However, in the middle reaches of this last river, several node‐5 associated samples with

characteristic high values of Ag, Cr, Bi Y and Sb (not shown in figure 2), suggest the presence of Agmineralised areas. Concordantly this part of the catchment coincides with historical Ag mining,

located mainly in the Graben de la Plata (Graben of Silver) area [8].

Lluta fluvial system

The analysis of the Lluta dataset resulted in a neural map composed of eight interconnected nodes

(Figure 3). As for the previous fluvial systems, along the Lluta fluvial system it is possible to

distinguish different node‐associations. In particular, the 1‐5‐6 node association is restricted to a

specific area located in the upper reaches of the catchment, which coincides with the Putre‐

Vilañuñumani hydrotermal alteration zone (Figure 3). The associated sediments present a

conspicuous geochemical signature of high concentrations of Au‐Hg‐As‐Mo‐S‐Bi‐Se‐W‐Ba (node 1),

U‐Rb‐K2O‐Be (node 5) and Be‐Cs‐As‐Al2O3‐HREE (node 6). These results suggest that the Putre‐Vilañuñumani hydrotermal alteration zone corresponds to one of the most important prospective

areas of the upper catchment of the Lluta fluvial system. The study of Baeza [9] has shown that it is

common to find primary U‐ bearing minerals in the stream sediments of this area. Therefore, this

part of the catchment could be a good candidate for being the source of the sedimentary U‐deposits

located downstream this catchment [9].

– 6 –



Figure 2 (a) Sample‐node distribution in the upper catchment of the Huasco fluvial system. Insert shows the GCS

neural Map. The samples present the same colour of the node to which they are associated. Geology modified from

the Geological Map of Chile [6]. (b) Visualisation of the distributions of each input variable. Scales at right indicate

whole rock concentrations (wt%) for major oxides and part per million (ppm) for trace elements.

– 7 –



Figure 3 (a) Sample‐node distribution in the upper catchment of the Lluta fluvial system (modified from [9]).

Insert shows the GCS neural Map. Only the nodes relevant for this part of the catchment are coloured. The

samples present the same colour of the node to which they are associated. Satellite image from Google Earth.

(b) Visualisation of the distributions of each input variable. Scales at right indicate whole rock concentrations

(wt%) for major oxides and part per million (ppm) for trace elements.

– 8 –



Figure 4 (a) Sample‐node distribution in the Limarí fluvial system. Insert shows the GCS neural Map. The

samples present the same colour and symbol of the node to which they are associated. Geology modified from

the Geological Map of Chile (Sernageomin, 2004). (b) Visualisation of the distributions of each input variable.

Scales at right indicate whole rock concentrations (ppm).

– 9 –



Limarí fluvial system

The analysis of the Limarí dataset resulted in a neural map composed of eight interconnected nodes

(Figure 4). As for the Lluta fluvial system, in the Limarí fluvial system it is also possible to distinguish a

very conspicuous area. This corresponds to the upper reaches of the Hurtado river, which is

characterised by the 8‐2 node association (Figure 4). The sediments of this area present typical highvalues of Cu‐Mo‐Ni‐Co‐ZnAs‐Be‐Bi‐Cd‐Mn‐U‐Sn, coupled with high concentrations of REE (not shown

in figure 4). This geochemical signature suggests the presence of an important altered/mineralised area

at the upper reaches of the Hurtado river. In agreement with the suggestions of previous studies [10],

this area most probably coincides with the alteration zone associated with the Coipita prospect.

CONCLUSIONS

The proposed methodology, based on dynamic growing self organising maps, was successful in

distinguishing the geochemical characteristics of specific segments of the studied fluvial systems. In

particular, it enables the recognition of the impact of mine and agro‐industrial activities,

downstream the Rapel fluvial system. The method also shows a considerable potential for revealingthe relevant geochemical characteristics of prospective areas, located in the upper reaches of the

Lluta, Limarí and Huasco fluvial systems. Those chemical signatures can be used as mineral

exploration guidelines based on geochemical data of active stream sediment.

REFERENCES

Fletcher, W.K. (1997) Stream Sediment Geochemistry in Today’s Exploration World. In: Proceedings of Exploration 97:

Fourth Decennial International Conference on Mineral Exploration. Edited by A.G. Gubins, pp. 249‐260. [1]

Agnew P.D. (1999) Interpretation and Visualisation of Soil and Rockchip Geochemistry, Sepon Project, Laos. In:

Exploration Geochemistry for the New Millennium, AIG Bulletin 3, pp. 113‐123. [2]

Kohonen, T. (1995) Self‐Organising Maps. Springer‐Verlag. [3]

Fritzke, B. (1994) Growing cell structures–A self ‐organizing network for unsupervised and supervised learning. Neural

Networks, vol. 7, pp. 1441‐1460. [4]

Lacassie, J. P. (2009) Estudio mineralógico y geoquímico del sistema fluvial del río Rapel, VI Región, Chile. Informe

Registrado Sernageomin, IR‐08‐37, 69 p., 10 figuras, 2 tablas, Santiago. [5]

Sernageomin (2004) Mapa Geológico de Chile Escala 1:1,000,000. [6]

Chouinard, A.,Williams‐ Jones, A. E., Leonardson, R. W., Hodgson, C. J., Silva, P., Tellez, C., Vega, J. & Rojas, F.

(2005) Geology and Genesis of the Multistage High‐Sulfidation Epithermal Pascua Au‐ Ag‐Cu Deposit, Chile

and Argentina. Economic Geology, May 1, 100(3), pp. 463‐490. [7]

Nasi, C., Moscoso, R. & Maksaev, V. (1990) Hoja Guanta: Regiones de Atacama y Coquimbo, escala 1:250.000. In:

Carta Geológica de Chile, No.67, 141p. [8]

Baeza, L. (2010) Estudio ambiental y económico: análisis mineralógico y geoquímico de sedimentos del sistema fluvial del

río Lluta, XV Región de Arica y Parinacota, Chile. Memoria de Título, Departamento de geología,

Universidad de Chile. [9]

Oyarzún, R., Oyarzún, J., Lillo, J., Maturana, H. & Higueras, P. (2006) Strong Metal Anomalies in Stream Sediments

from Semiarid Watersheds in Northern Chile: When Geological and Structural Analyses Contribute to

Understanding Environmental Disturbances. International Geology Review, Vol. 48, 2006, pp. 1133‐1144. [10]

– 10 –

lacassie et al 2011 geomin article reduced

Documents