Characterization of alteration and mineralization of Chimú Formation quartzite with nearinfrared

Transcription

1 Characterization of alteration and mineralization of Chimú Formation quartzite with nearinfrared spectroscopy Lagunas Norte, Alto Chicama District, Northern Peru. M.M. van der Linde Master of Science Thesis Department of Geoscience and Engineering

2 Title : Characterization of alteration and mineralization of Chimú Formation quartzite with near-infrared spectroscopy. Author(s) : Machiel Maarten van der Linde Date : May 2015 Supervisor(s) : Dr. M.W.N. Buxton TU Delft RE Dr. F.J.A. van Ruitenbeek ITC Ir. M. Dalm TU Delft RE Dr. K.H.A.A. Wolf TU Delft AG TA Report number : n/a Postal Address : Telephone : Telefax : Section for Resource Engineering Department of Applied Earth Sciences Delft University of Technology P.O. Box 5028 The Netherlands (31) (secretary) (31) Copyright 2015 Section for Resource Engineering All rights reserved. No parts of this publication may be reproduced, Stored in a retrieval system, or transmitted, In any form or by any means, electronic, Mechanical, photocopying, recording, or otherwise, Without the prior written permission of the Section for Resource Engineering

3 Characterization of alteration and mineralization of Chimú Formation quartzite with near-infrared spectroscopy Lagunas Norte, Alto Chicama District, Northern Peru. Master of Science Thesis For the degree of Master of Science in Resource Engineering at Delft University of Technology Full version is under embargo - CiTG-RE section M.M. van der Linde May 22, 2015 Faculty of Civil Engineering and Geosciences (CiTG) Delft University of Technology

4 Copyright Geosciences and Engineering All rights reserved.

5 Abstract The use of sensor based technology to distinguish in real-time between economic and subeconomic material could contribute to the optimization of the technology and economics behind mining operations. The goal of this study is to contribute to the development of a near-infrared sensor-based pre-sorting ore classification model. This study is focused on the 11.6 Moz Lagunas Norte high sulphidation epithermal gold deposit in the La Libertad Region, Peru and is part of a research collaboration between Barrick Gold and Delft University of Technology. In the deposit 80 % of the gold is hosted in weakly metamorphosed quartzite of the Upper Jurassic to Lower Cretaceous Chimú Fm. A correlation between alteration mineralogy and mineralization is present according to previous authors. In this study a sample set originated from 6 drill holes throughout the deposit was investigated to determine the nature of the correlation between alteration mineralogy, Au and sulphide content by using near-infrared reflectance (NIR) spectroscopy. This method was chosen since alteration minerals typical for the deposit have diagnostic features in this range. Alteration mineralogy was determined with near-infrared reflectance spectroscopy for a whole rock sample suite. This resulted in the identification of mineralogy, typically observed in the advanced argillic zone of a high sulphidation epithermal deposit. The mineralogy was verified by XRD analyses. Furthermore iron bearing mineralogy, typically found in the oxidised breccia zone, was identified. Principal component analyses on the spectra resulted in the observation that the mineral compositions are mixtures between mineral end members, which can be coupled to progressively neutralising of acidic fluids in the hydrothermal system. Subsamples were extracted and were tested both spectrally, petrographic, and geochemical. This resulted in the conclusion that Au was focussed in fault zones. Partial least squares discriminant analyses was used to classify samples based on Au mineralization revealing that pyrophyllite is an indication for Au<0.25 ppm. Total sulphur based classification models were able to distinguish samples which contained total sulphur>0.25%. The Au model had an accuracy of 76 % adjusting the threshold would result in a small class which has a high certainty to not contain gold. Hence PLS-DA models showed it is possible to classify ore, based on the near-infrared reflectance spectrum. In which alteration mineralogy can be used as a calibrated proxy for gold and sulphide content at Lagunas Norte. Implementing a pre-sorting step based on NIR would result in a reduction of processed waste, which would result in a reduction of operational expenditure.

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7 Table of Contents Table of Contents List of Figures List of Tables v ix xiii 1 Introduction General introduction Problem definition Study methodology Data sets Goal Hypotheses Purpose Outcomes Research scope and limitations Geology Literature review General characteristics of epithermal ore deposits High sulphidation versus low sulphidation General characteristics of high sulphidation Hydrothermal fluids Alteration mineralogy Gold deposition mechanism Study area Regional geology of northern Peru Geology of the Lagunas Norte deposit Hydrothermal alteration

8 vi TABLE OF CONTENTS 3 Near-infrared spectroscopy General introduction Absorption processes Electronic processes Vibrational processes Sensitivity to crystal structures and chemistry Scattering process Influence of mixtures and grain size Spectral feature recognition Applicability of near-infrared spectroscopy in this study Methodology Sample selection and preparation Data acquisition Spectral data acquisition Verification of identified mineralogy Geochemical data Spectral data processing Data analysis Principal component analyses (PCA) Partial least squares discriminant analyses (PLS-DA) Results Mineralogy determined with near-infrared spectroscopy Mineralogy measured in Lagunas Norte dataset Diagnostic absorption features Non diagnostic absorption features Principal component analyses of spectral data Conclusions on mineralogy Spatial context of mineralization Mineralization Phase B geochemical testing Petrographic analyses Conclusions on mineralization Classification of samples based on near-infrared reflectance spectra PLS-DA model PLS-DA model to classify based on Au grade PLS-DA model to classify based on total sulphur content Correlation of mineralogy with mineralization Correlation Conclusions

9 TABLE OF CONTENTS vii 6 Conclusions and recommendations Conclusions Recommendations for future work Bibliography 113 A Near-infrared measurements 117 A.1 Near-infrared equipment A.2 Electronic supplementary data B Phase A - verification of mineralogy and mineralization 119 C Phase B - verification of mineralogy and mineralization 121

10 viii TABLE OF CONTENTS

11 List of Figures 1-1 Geographic location of the Lagunas Norte deposit Lagunas Norte mine map Lagunas Norte section NW Mineralogical zonation in low/high sulphidation type Schematic cross section of the origin of hydrothermal acidic fluids Cross section through a typical high sulfidation orebody zonation Thermal stability of various hydrothermal minerals Geological map northwestern Peru Geological map of Lagunas Norte Stratigraphic column of Lagunas Norte Cross section though the deposit Hydrothermal alteration of Lagunas Norte Terminology related to spectroscopic measurements Electromagnetic spectrum Charge transfer absorptions for goethite and hematite Vibrations of molecule bonds Scattering in quartzite Near-infrared spectrum of intimate mixture of goethite alunite Influence of particle size on spectrum Spectral feature recognition Phase A lab testing setup Phase B lab testing setup Mineral spectra SWIR region

12 x LIST OF FIGURES 5-2 Mineral spectra VIS region Minimum location of the diagnostic double feature of alunite Reflectance spectra of alunite Minimum location of the diagnostic features of pyrophyllite Reflectance spectra of pyrophyllite mixed with diaspore Minimum location of the diagnostic doublets of dickite Reflectance spectra of dickite Location of minimum of broad water feature Sample used to remove water feature SWIR spectrum removal of water feature SWIR spectrum of wet sample PCA score values phase A PCA loading plot phase A Borehole log DDH Borehole log DDH Borehole log DDH Borehole log DDH Borehole log DDH Borehole log DDH Borehole log DDH Borehole log DDH Borehole log DDH Borehole log DDH Borehole log DDH Borehole log DDH Borehole log DDH Verification of Au grade Geochemistry on phase B subsamples Au mineralization on small scale Petrographic analyses Petrographic analyses Overview of PLS-DA testing Predictive power of PLS-DA model Error rate vs. nr of latent variables for PLS-DA Au model Error rate vs. nr of latent variables for PLS-DA Au model Predicted responses of PLS-DA model on Au-0.25-ss-individual Predicted responses of PLS-DA model on Au-0.25-pdr-average Loadings of LV 1 and LV 2 of PLS-DA model: Au-0.25-ss-individual Scores of PLS-DA model: Au-0.25-ss-individual

13 LIST OF FIGURES xi 5-41 Loadings of LV 1 and LV 2 of PLS-DA model: Au-0.25-pdr-average Scores of PLS-DA model: Au-0.25-pdr-average Error rate vs. nr of latent variables for PLS-DA total sulphur model Error rate vs. nr of latent variables for PLS-DA total sulphur model Predicted responses of PLS-DA model on tot-s-0.25-ss-individual Predicted responses of PLS-DA model on tot-s-0.25-pdr-average Loadings of LV 1 and LV 2 of PLS-DA model: tot-s-0.25-ss-individual Scores of PLS-DA model: tot-s-0.25-ss-individual Loadings of LV 1 and LV 2 of PLS-DA model: tot-s-0.25-pdr-average Scores of PLS-DA model: tot-s-0.25-pdr-average B-1 Phase A - verification of mineralogy and mineralization C-1 Phase B - verification of mineralization C-2 Phase B - verification of mineralization C-3 Phase B - verification of mineralization C-4 Phase B - verification of mineralization

14 xii LIST OF FIGURES

15 List of Tables 1-1 Summary of study setup Alteration assemblages Identified mineralogy SWIR Identified mineralogy VIS SWIR minerals vs. mineralization Sample classes Confusion matrix Confusion matrix of Au PLS-DA models on validation subset Confusion matrix of predictive power of subsample Au model Confusion matrix of Au PLS-DA models on validation subset

16 xiv LIST OF TABLES

17 Chapter 1 Introduction 1.1 General introduction During the last decades improved recovery methods and favourable long term commodity prices enabled the exploitation of many low grade epithermal gold deposits (Simmons et al., 2005). Commodity prices are currently under pressure, which combined with a strong increase in cost of mining and decreasing ore grades, is putting pressure on mining companies to optimise their operations. Utilising sensor based technology for real-time characterisation of geochemical, mineralogical, and physical material characteristics could be beneficial for optimising mine production (Buxton & Benndorf, 2013). This study contributes to the development of these techniques. This study is focussed on the Lagunas Norte high sulphidation epithermal style gold deposit, Alto Chicama property in the La Libertad Region, north-central Peru (7 56 S, W) and is part of a research collaboration between Barrick gold and Delft University of Technology. The deposit contains 11.6 Million ounce 1 and is hosted in weakly metamorphosed quartzite of the Upper Jurassic to Lower Cretaceous Chimú Formation and in overlying Miocene volcanic rocks of dacitic to rhyolitic composition (Cerpa et al., 2013). The location of the deposit is shown in figure 1-1. Alteration mineral assemblages are important for the understanding of hydrothermal ore deposits and can be mapped using near-infrared spectroscopy (Thompson et al., 1999). The limited reactivity of quartzite to hydrothermal solutions resulted in important challenges regarding the mapping of alteration and thus the relatively recent discovery of the deposit despite the fact it is well exposed on the surface (Montgomery, 2012; Cerpa et al., 2013). The term "epithermal" derives from the genetic classification scheme for hydrothermal ore deposits proposed by Lindgren (1933). The scheme refers to a range of temperature versus depth (i.e. pressure) ore forming conditions, mainly subaerial, that develop within 1 Contained gold: 2.8 Moz reserves (proven and probable), 0.4 Moz resources (measured and indicated), and 8.4 Moz total mined gold in the period 2005 to 2014 (reserves and resources reported at December 31, 2014 by Barrick Gold Corporation (2013, 2014a)).

18 2 Introduction "W "W 9 % 8 0 0"S 8 0 0"S "W "W "W "N 0 0 0" "S "S "S "S "W Kilometers 60 ± "W Legend: 9 % Lagunas Norte Figure 1-1: Geographic location of the Lagunas Norte deposit (Instituto Geologico Minero y Metalurgico, 1999).

19 1.2 Problem definition 3 much larger hydrothermal systems. Epithermal style deposits are host to both base and precious metals but are currently predominantly mined for their gold and silver content. The six largest deposits or districts (i.e. Chinkuashih, El Indio, Goldfield, La Coipa, Lepanto, and Pueblo Viejo) each contain more than 100 tonnes of gold (Arribas, 1995). The genesis of epithermal style gold and silver deposits has been studied extensively during the last decades (Hedenquist, 1987; Arribas, 1995; Sillitoe, 1999; Cooke & Simmons, 2000; Hedenquist, 2000; Simmons et al., 2005). Epithermal deposits are formed in the vicinity of large scale hydrothermal discharge zones due to locally favourable conditions (e.g. lithology, fault zones, brecciation). In order to locate zones within a deposit where mineralization is present, a reconstruction of ancient fluid pathways within the hydrothermal system is essential (Simmons et al., 2005). Geological interpretation of hydrothermal systems is often difficult since the deposit can originate from a series of overprinting stages. Fluid composition within a hydrothermal system is important for formation of a deposit and is influenced by both meteoric and magmatic end members. Changing pressure and temperature conditions also play an important role in deposition of minerals (Einaudi et al., 2003). Moreover host rock permeability is affected by mineral precipitation and dissolution due to hydrothermal processes (Cerpa et al., 2013). For example a fluid with high gas content that is subjected to a sharp pressure reduction can result in a hydrothermal eruption thus creating permeable breccia zones (Cooke & Simmons, 2000). 1.2 Problem definition Decreasing ore grades and increasing cost of mining are demanding a response from the industry. Implementing sensor based sorting techniques as a pre-sorting step in the mining cycle could result in a decrease of cost by reducing the initial high variability within the material (i.e. post-mining). The feasibility of sensor based sorting is dependant on the ability of an appropriate sensor to distinguish, in real-time, between economic and sub-economic material. Large volume flows are common in mining operations therefor a balance has to be found between accuracy and throughput (Buxton & Benndorf, 2013). At Lagunas Norte operation costs were calculated as US$ 2.69 per tonne of material mined and US$ 2.84 per tonne of material processed (based on NI , Roscoe Postle Associates (2012)). On a yearly production of 50 Mt mined material and 22 Mt processed material (annual production in 2014, Barrick Gold Corporation (2014a)) a post-mining sensor based selection step could reduce processing (e.g. 5% reduction in the process feed by pre-sorting yields a US$ 3.1 million saving annually as a result of reduction of processed waste). Furthermore water scarcity will be of increasing importance for the licence to operate for mining companies. Implementing pre-sorting could contribute to achieve the reduction of water usage. Introducing a sensor based sorting step in an epithermal style gold deposit is challenging since the gold content per tonne is very low and very finely distributed making direct detection of the contained gold impossible. Gold grade could possibly be estimated by measurement of a calibrated proxy. Currently such a system does not exist for these type of deposits hence this research is aimed in contributing to that development.

20 4 Introduction 1.3 Study methodology This section describes a detailed study approach and presents the data sets used. This study of the Lagunas Norte deposit is structured as a logical framework with three levels (i.e. goal, purpose, and outcomes). The study goal and hypothesis are at the top level of the framework and present the "bigger picture" in which this study strives to contribute. Below this level a purpose is defined specifically for this study, which on itself consist of five specific outcomes presented in the bottom level of this framework. The outcomes are specific research objectives which combined should fulfil the purpose of the study and contribute to the study goal. The study setup is summarised as a full page table at the end of this chapter in table Data sets The following data sets originating from the Lagunas Norte deposit were used in this study: A sample suite of 277 whole rock core samples obtained from 6 drill hole locations sampled by Dalm (2013) made available by Barrick. On average the rock samples where cm long HQ-size 2 half cores. Drill hole assay data of the intervals from which the samples originated provided by Barrick. Drill hole assay values were based on fire assay and ICP tests on a 1.5 m interval. The location of the sampled drill holes is presented in figure 1-2 an shows the spatial relevance of this study with regards to the Lagunas Norte deposit. The blue line is indicating the section line which is presented in figure 1-3. In the presented section the hole path of the sampled drill holes was determined based on the available sections provided by Barrick in the technical reports of the mine (e.g. NI , (Roscoe Postle Associates, 2012)). In these sections the drill holes have an apparent inclination due to projection on section lines. The current section is parallel to these sections in order to exclude errors due to the need for projection of the drill hole on a different section line Goal This study is aimed at contributing to the development of a sensor based pre-sorting model that can control dispatch decisions which result in allocation of material to specific destinations within an operation (e.g. waste dump and feed-stockpiles). This study is focussed on the Lagunas Norte deposit and in particular the Chimú Formation, which is host to about 80 percent of gold reserves at Lagunas Norte (Montgomery, 2012). Thompson et al. (1999) and Ducart et al. (2006) conclude that near-infrared spectroscopy can be used effectively for the mapping of alteration mineralogy, which is related to alteration mineralogy according to Cerpa et al. (2013). - Objective: To develop a near-infrared sensor based pre-sorting ore classification model to reduce high initial material variability and generate a more homogeneous product. 2 HQ core samples have a 63.5 mm diameter

21 1.3 Study methodology 7 - Success indicator: To accurately discriminate between economic and sub-economic material (i.e. cut off grade) by reducing the material variability and generating a more homogeneous product before processing. - Verification method: Classify whole rock samples based on their near-infrared spectrum with the proposed model and check the sorting performance of the model with geochemical analyses. - Assumptions: Near-infrared sensor data can be used to classify material by measurement of a calibrated proxy (i.e. alteration mineralogy) and correlate this to the geochemical properties of the material Hypotheses Distribution of gold and alteration zonation are both mainly controlled by permeability of the host rock (Cerpa et al., 2013). This suggests a correlation is present between observed alteration mineralogy and Au and sulphide content. Hence the hypothesis of this study is formulated as follows: Alteration mineralogy in the Chimú Formation is correlated with the Au and sulphide content and can therefore be used as a calibrated proxy in a classification model Purpose The genetic model proposed by Cerpa et al. (2013) implies that the paragenetic sequence can be determined based on alteration mineralogy and mineralization is restricted to three stages within this sequence. Hence the purpose of this thesis is to investigate the relationship between alteration mineralogy and mineralization. - Objective: Determine the nature of the correlation between alteration mineralogy, Au and sulphide content in the Chimú Formation quartzite by using near-infrared spectroscopy. - Success indicator: Establish insight in the relationship between gold distribution and alteration mineralogy. Develop a model to use near-infrared spectral data as a calibrated proxy to classify whole rock samples. - Verification method: Cross validation of the model performance. Verify the influence of lithology, moisture, and particle size on testing and prediction capabilities of the model. - Assumptions: Drill hole samples are representative of the diversity in alteration and mineralization within the deposit. Geochemical data is accurate on the scale of testing to verify the sorting performance of the model.

22 8 Introduction Outcomes 1. - Objective: Define the deposit mineralogy and characteristics of alteration in the Chimú Formation quartzite. - Success indicator: Literature review of regional and local geology and mineralization of the deposit. Description of mineralogy associated with both the original lithology as well as minerals associated with overprinting as a result of hydrothermal alteration. - Verification method: Literature review of state-of-the-art available material on high sulphidation style epithermal deposits. - Assumptions: Available literature specific to this deposit is limited to Cerpa et al. (2013) and Montgomery (2012) Objective: Differences between alteration and supergene oxidation in the Chimú Formation. - Success indicator: Description of supergene oxidation based on available literature of high sulphidation epithermal style deposits and literature specific to the deposit. - Verification method: Verify if mineralogy which is associated with alteration and supergene oxidation according to literature is observed within the data set. - Assumptions: Alteration and supergene oxidation observed in the test data is representative for the whole deposit Objective: To determine alteration mineralogy of the Chimú Formation using nearinfrared spectroscopy. - Success indicator: Identify alteration mineralogy based on near-infrared reflectance spectra measured on the data set. - Verification method: Mineral identification on a subset of samples where mineralogy is measured using quantitive X-ray diffraction (Q-XRD). Five samples were selected for additional petrographic analyses. - Assumptions: Near-infrared spectra were obtained on spot locations hence an operator biased preference is introduced. Minerals which don t contain diagnostic features in the near-infrared spectrum can t be classified Objective: To develop a model to classify Chimú Formation quartzite based on the near-infrared spectral signature of alteration mineralogy. - Success indicator: Classify samples based on their near-infrared spectral signature. - Verification method: Cross correlate classifying model on verification dataset to check performance. - Assumptions: Samples should contain diagnostic features in the near-infrared spectrum, which can be used for classification. A lack of diagnostic features in samples could make classification based on their NIR spectrum impossible Objective: To correlate mineralogical alteration of the Chimú Formation with Au and sulphide content. - Success indicator: Construct a model which could classify samples on their geochemical properties based on their near-infrared spectral signature. - Verification method: Cross correlation to measure performance of the model on both calibration and validation dataset. - Assumptions: The test data sample population represents the whole deposit hence an observed correlation is valid throughout the deposit.

23 1.3 Study methodology Research scope and limitations This study investigates the applicability of using a near-infrared sensor based model for the discrimination between economic and sub-economic material in the Lagunas Norte deposit. The proposed methodology could be applied on different deposits but is limited in this study to the Lagunas Norte deposit. For the implementation of a near-infrared based model on a deposit infrared-active minerals which are characteristic for mineralization need to be present in the deposit. Included in this study: - Literature review of the Lagunas Norte high-sulphidation epithermal style deposit. - Analyses of the near-infrared spectral signature of alteration minerals measured in the deposit. - Determination of the nature of the correlation between alteration and mineralization in the Chimú Formation quartzite with the following statistical methods: principal component analyses and partial least squares discriminant analyses. Excluded in this study: - Analyses of spectral signature of characteristic alteration minerals which were not measured in the deposit. - Other statistical methods were not used to investigate the relationship between spectral data and mineralization. - Analyses of the correlation between mineralization and alteration in other high-sulphidation epithermal style gold deposits. - limited access to the deposit model prevented cow;ling near-infrared data to production data of the mine..

24 10 Introduction objectives succes indicator verification method assumptions Goal: Develop a near-infrared sensor based pre-sorting ore classification model. Purpose: Determine the nature of the correlation between alteration and Au and sulphide by using NIR spectroscopy. Outcomes: 1. Define alteration in the Chimú Formation. 2. To differentiate between hydrothermal alteration and supergene oxidation. 3. To determine alteration mineralogy of the Chimú Formation using NIR spectroscopy. 4. To classify Chimú Formation quartzite based on the NIR signature. 5. To correlate mineralogical alteration of the Chimú Formation with Au and sulphide content. - Accurately discriminate between economical and sub-economic material. - Insight in the relationship between Au and alteration mineralogy. - Develop a model to use NIR data as a calibrated proxy to classify whole rock samples. - Overview of regional and local geology and mineralization. - Description of mineralogy. - Description of mineralogy based on literature. - Identify alteration mineralogy based on NIR spectral data. - Classification model based on their NIR spectral signature. - Classify samples on geochemical properties based on their NIR signature. - Classifying whole rock samples with known composition. - cross validation of the model prediction capabilities. - Influence of lithology, moisture, and particle size. - Literature review on high sulphidation style epithermal deposits. - Verify identified mineralogy associated with literature. - Mineral identification of a subset of samples using Q-XRD. - Petrographic analyses. - Cross validate classification model to check performance. - Cross validation to measure model performance. - NIR data can be used to classify material either by direct measurement or by measurement of a calibrated proxy. - drill hole samples represent the diversity in alteration and mineralization of the deposit. - Geochemical data is accurate on the scale of testing. - Availability of literature specific to this deposit is limited - Alteration and supergene oxidation observed in the test data is representative for the whole deposit. - NIR spectra test locations are operator biased. - Minerals with no diagnostic features can t be classified. - Samples have diagnostic NIR features. - Samples represent the whole deposit hence an observed correlation is valid throughout the deposit. Table 1-1: Summary of study setup.