Nouveaux développements en géostatistique minière. New developments in mining geostatistics
|
|
- Theodore Baldwin
- 5 years ago
- Views:
Transcription
1 Ressources Minérales : la vision du Mineur Février 2012 Nouveaux développements en géostatistique minière New developments in mining geostatistics Jacques Rivoirard Responsable de l Équipe Géostatistique Centre de Géosciences
2 2 Multi-facies block models Domaining by clustering multivariate geostatistical data Variances for mineral resources classification High grade values and topcut
3 Multi-facies block models 3
4 4 How to estimate or simulate block grades when different facies are mixed? Convenio with Codelco, application to porphyry copper deposits S. Séguret (GEOMIN 2011) H. Beucher
5 CURRENT PRACTICE (1) Drilhole 1 Drilhole 2 Data Geological interpretation Proportions by block Block grades
6 CURRENT PRACTICE (2) Each block V is divided into facies (proportions p i = v i /V) n Z(V)= p Z( ) i=1 i v i * * * i n Z (V)= p Z ( ) i=1 v i
7 7 Here facies are alteration units UNITS (1&2) Unit 303, transitional zone.
8 8 alteration units 305 CMH Chloritic marginal low grade 318 PF Background potassic 312 Qz-Mo Quartz- Molibdenite 303 ZT Transition zone 301 QSP Pervasive Quartz Sericitic 307 PI Intense potassic 3092 PIR-East Intense Relict Potasic (East part) 3091 PIR-West Intense Relict Potasic (West part)
9 Geostatistical tools to analyze transitions between facies γ p(x i, x+h i) i (h) γ ij (h) p(x i, x+h j) γ γ ij i (h) (h) p(x+h j x i, x+h i) γ iz i γ i (h) (h) E[Z(x+h) x+h i, x i]
10 10 Preferential transitions from one facies to any other (per direction) N-S E-W vertical To From
11 11 Example of interpretation: Preferential transitions along direction
12 Average Cu grade when entering within unit
13 Comparison between estimated Cu block grades: -Traditional -Cokriging facies and their partial grades 13
14 14 Conclusion: Geostatistical tools: Analysis of transitions between facies Analysis of grades at transitions Multivariate models for facies indicators and grades: allows estimation of facies proportions and grades Also possibility to simulate facies and grades Pluri-gaussian truncated model for facies (work by Hélène Beucher) Developments to be done to take into account explicitly non-stationarities
15 15 Domaining by clustering multivariate geostatistical data
16 16 Domaining = define domains that are homogeneous with respect of a number of parameters (rock type, alteration, grades ) time-consuming rapidly leads to a high combinatory Tool under development: statistical classification method in spatial context to help defining domains (i.e. clusters of data) Geological and Geostatistical Domaining Consortium (G2DC) by Géovariances with MINES-ParisTech Romary et al., 2012
17 Toy example 17 exhaustive (unknown) sample data How to classify sample data into 2 domains? (and then: delineate these domains)
18 Toy example 18 Examples of classification into 2 domains Classical k-means method based on (x,y,z) Method developed: ascending hierarchical method + spatial connections (iterative clustering of data or groups of data presenting similarities and spatially connected)
19 Uranium deposit dataset 19 Flat, elongated deposit at the contact between socle and sandstone Variables considered: Coordinates: X, Y and Z uranium grades geological factor: socle or sandstone hematization degree
20 Results of clustering data down to 6 clusters 20
21 21
22 22 The differentiation between black and red is only due to grade. Black high grades are intrincated with neighboring red lower grades We would like to mix those, but next iteration would mix black and cyan!
23 23 At present the method provides a help to domaining, but a number of developments are still to be done: Investigate different ways to connect points in 3D Identify and merge intrincated clusters Merge clusters that would give a stationary set How to treat missing values of some variables? Another point is the delineation of such clusters, when these are identified
24 24 Variances for mineral resources classification
25 25 Mineral resources classification: Largely subjective, depending on the competent person Where possible, uncertainties to be assessed by objective tools
26 26 Geostatistics to assess uncertainties coming from spatial sampling: Repeated geostatistical conditional simulations of the deposit Powerful but demanding in hypotheses See presentation by Deraisme Estimation variances for set of blocks, application to porphyry copper deposits (Codelco convenio), Rivoirard and Chilès (2011)
27 27 Example: mean grade Z(V) within a given volume V Z(V)* The estimation variance summarizes the uncertainty of Z(V) around its estimate Z(V)*
28 28 Estimation variance for a set of blocks Easy but incorrect way: combine block variances, e.g. kriging variances Block errors are correlated unless blocks are estimated by inner samples only kriging variance considers data location, but actual variances tend to be higher where grades are higher Model and apply the proportional effect between variances and means
29 Porphyry copper deposit The variability of sample grades within a block increases with their mean grade (proportional effect) samples standard deviation samples mean
30 Modeling the proportional effect : not straightforward, as the variability is also known to depend on the number and location of samples in the block > Estimation of a local variability factor, depending on variogram and local mean 30
31 kriging std Such a local variability factor can be applied to customize estimation variances 6 6 Example: 5 10 big blocks in a high grade zone 4 combining classical kriging variances: 4 3 CV = 5% estimation from inner samples + proportional effect: 1 1 CV = 12% 0 Before customization rho= customized kriging standar After customization kriging from inner data kriging from inner data 31
32 Example: 10 big blocks in a high grade zone combining classical kriging variances: CV = 5% estimation from inner samples + proportional effect: CV = 12% 32
33 33 Conclusion: Easy combination of variances for any set of informed blocks Modeling proportional effect through a local variability factor to be validated on simulations (reference known)
34 High grade values and topcut 34
35 35 Part of the general problem of handling high grade values Frequencies Collaboration with Cogéma Aréva ( ) Rivoirard, Demange, Freulon, Lécureuil, Bellot, to be published 2012
36 Topcut 36 A current practice in gold deposits (skew histogram, with few high values) One truncates the grade Z(x), and estimates the truncated grade: min(z(x), z e ), minimum between grade and topcut value z e Estimated block grades are more realistic but: Where has gone the missing metal? How to choose the topcut value z e? z e
37 Topcut model 37 Z(x) = min(z(x), z e ) + [m(z e )- z e ] 1{Z (x) > z e } + R(x) = truncated + indicator + residual variable of presence of high values Topcut value z e : chosen to put the highest amount of nugget variability in the residual Estimation: based on the truncated variable and on the indicator The model distributes the missing metal where the high grades have the higher chance to be located
38 Estimations (vertical sections) 38 Presence of values > topcut (indicator) 10m 20m Truncated grade Grade estimated using topcut model Kriged grade Fig Xavier Freulon
39 39 Conclusion Robust estimation, albeit taking high values into account Intermediary between ordinary kriging and non-linear techniques A step towards other, more general and more flexible, non-linear techniques
40 Thank you FLUMY Mines-ParisTech
Is there still room for new developments in geostatistics?
Is there still room for new developments in geostatistics? Jean-Paul Chilès MINES ParisTech, Fontainebleau, France, IAMG 34th IGC, Brisbane, 8 August 2012 Matheron: books and monographs 1962-1963: Treatise
More informationA MultiGaussian Approach to Assess Block Grade Uncertainty
A MultiGaussian Approach to Assess Block Grade Uncertainty Julián M. Ortiz 1, Oy Leuangthong 2, and Clayton V. Deutsch 2 1 Department of Mining Engineering, University of Chile 2 Department of Civil &
More informationOptimizing Thresholds in Truncated Pluri-Gaussian Simulation
Optimizing Thresholds in Truncated Pluri-Gaussian Simulation Samaneh Sadeghi and Jeff B. Boisvert Truncated pluri-gaussian simulation (TPGS) is an extension of truncated Gaussian simulation. This method
More informationKriging, indicators, and nonlinear geostatistics
Kriging, indicators, and nonlinear geostatistics by J. Rivoirard*, X. Freulon, C. Demange, and A. Lécureuil Synopsis Historically, linear and lognormal krigings were first created to estimate the in situ
More informationDrill-Holes and Blast-Holes
Drill-Holes and Blast-Holes Serge Antoine Séguret 1 and Sebastian De La Fuente 2 Abstract The following is a geostatistical study of copper measurements on samples from diamond drill-holes and blast-holes.
More informationRéservoirs complexes: modélisation stochastique et génétique Stochastic and process-based models for heterogeneous reservoirs
Réservoirs complexes: modélisation stochastique et génétique Stochastic and process-based models for heterogeneous reservoirs Jacques RIVOIRARD Equipe Géostatistique Centre de Géosciences 1 A common issue
More informationProduction reconciliation of a multivariate uniform conditioning technique for mineral resource modelling of a porphyry copper gold deposit
Production reconciliation of a multivariate uniform conditioning technique for mineral resource modelling of a porphyry copper gold deposit by W. Assibey-Bonsu*, J. Deraisme, E Garcia, P Gomez, and H.
More informationTRUNCATED GAUSSIAN AND PLURIGAUSSIAN SIMULATIONS OF LITHOLOGICAL UNITS IN MANSA MINA DEPOSIT
TRUNCATED GAUSSIAN AND PLURIGAUSSIAN SIMULATIONS OF LITHOLOGICAL UNITS IN MANSA MINA DEPOSIT RODRIGO RIQUELME T, GAËLLE LE LOC H 2 and PEDRO CARRASCO C. CODELCO, Santiago, Chile 2 Geostatistics team, Geosciences
More informationExperimental variogram of the residuals in the universal kriging (UK) model
Experimental variogram of the residuals in the universal kriging (UK) model Nicolas DESASSIS Didier RENARD Technical report R141210NDES MINES ParisTech Centre de Géosciences Equipe Géostatistique 35, rue
More informationCapping and kriging grades with longtailed
Capping and kriging grades with longtailed distributions by M. Maleki*, N. Madani*, and X. Emery* Synopsis Variogram analysis and kriging lack robustness in the presence of outliers and data with long-tailed
More informationReservoir characterization
1/15 Reservoir characterization This paper gives an overview of the activities in geostatistics for the Petroleum industry in the domain of reservoir characterization. This description has been simplified
More informationDefining Geological Units by Grade Domaining
Defining Geological Units by Grade Domaining Xavier Emery 1 and Julián M. Ortiz 1 1 Department of Mining Engineering, University of Chile Abstract Common practice in mineral resource estimation consists
More informationA full scale, non stationary approach for the kriging of large spatio(-temporal) datasets
A full scale, non stationary approach for the kriging of large spatio(-temporal) datasets Thomas Romary, Nicolas Desassis & Francky Fouedjio Mines ParisTech Centre de Géosciences, Equipe Géostatistique
More informationBasics in Geostatistics 2 Geostatistical interpolation/estimation: Kriging methods. Hans Wackernagel. MINES ParisTech.
Basics in Geostatistics 2 Geostatistical interpolation/estimation: Kriging methods Hans Wackernagel MINES ParisTech NERSC April 2013 http://hans.wackernagel.free.fr Basic concepts Geostatistics Hans Wackernagel
More informationCONDITIONAL CO-SIMULATION OF COPPER GRADES AND LITHOFACIES IN THE RÍO BLANCO LOS BRONCES COPPER DEPOSIT
CONDITIONAL CO-SIMULATION OF COPPER GRADES AND LITHOFACIES IN THE RÍO BLANCO LOS BRONCES COPPER DEPOSIT Alejandro Cáceres Geoinnova Consultores, Chile Xavier Emery Department of Mining Engineering, University
More information7 Geostatistics. Figure 7.1 Focus of geostatistics
7 Geostatistics 7.1 Introduction Geostatistics is the part of statistics that is concerned with geo-referenced data, i.e. data that are linked to spatial coordinates. To describe the spatial variation
More informationThe value of imperfect borehole information in mineral resource evaluation
The value of imperfect borehole information in mineral resource evaluation Steinar L. Ellefmo and Jo Eidsvik Abstract In mineral resource evaluation a careful analysis and assessments of the geology, assay
More informationQuantifying Uncertainty in Mineral Resources with Classification Schemes and Conditional Simulations
Quantifying Uncertainty in Mineral Resources with Classification Schemes and Conditional Simulations Xavier Emery 1, Julián M. Ortiz 1 and Juan J. Rodriguez 2 1 Department of Mining Engineering, University
More informationContents 1 Introduction 2 Statistical Tools and Concepts
1 Introduction... 1 1.1 Objectives and Approach... 1 1.2 Scope of Resource Modeling... 2 1.3 Critical Aspects... 2 1.3.1 Data Assembly and Data Quality... 2 1.3.2 Geologic Model and Definition of Estimation
More informationWhat is Non-Linear Estimation?
What is Non-Linear Estimation? You may have heard the terms Linear Estimation and Non-Linear Estimation used in relation to spatial estimation of a resource variable and perhaps wondered exactly what they
More informationThe Proportional Effect of Spatial Variables
The Proportional Effect of Spatial Variables J. G. Manchuk, O. Leuangthong and C. V. Deutsch Centre for Computational Geostatistics, Department of Civil and Environmental Engineering University of Alberta
More informationRadial basis functions and kriging a gold case study
Page Radial basis functions and kriging a gold case study D Kentwell, Principal Consultant, SRK Consulting This article was first published in The AusIMM Bulletin, December. Introduction Recent advances
More informationEntropy of Gaussian Random Functions and Consequences in Geostatistics
Entropy of Gaussian Random Functions and Consequences in Geostatistics Paula Larrondo (larrondo@ualberta.ca) Department of Civil & Environmental Engineering University of Alberta Abstract Sequential Gaussian
More informationGEOMETRIC MODELING OF A BRECCIA PIPE COMPARING FIVE APPROACHES (36 th APCOM, November 4-8, 2013, Brazil)
GEOMETRIC MODELING OF A BRECCIA PIPE COMPARING FIVE APPROACHES (36 th APCOM, November 4-8, 2013, Brazil) Serge Antoine Séguret; Research Engineer in Geostatistics; Ecole des Mines; Center of Geosciences
More informationThe presentation is entitled ANISOTROPY OF THE ROCK QUALITY DESIGNATION & ITS GEOSTATISTICAL EVALUATION».
It is my pleasure to present the work done jointly by my collaborator Doctor Serge Séguret from France, and myself, on data provided by my team and by my colleague Claudio Rojas The presentation is entitled
More informationTricks to Creating a Resource Block Model. St John s, Newfoundland and Labrador November 4, 2015
Tricks to Creating a Resource Block Model St John s, Newfoundland and Labrador November 4, 2015 Agenda 2 Domain Selection Top Cut (Grade Capping) Compositing Specific Gravity Variograms Block Size Search
More informationCarrapateena Mineral Resources Explanatory Notes April OZ Minerals Limited. Carrapateena Mineral Resources Statement April
OZ Minerals Limited Carrapateena Mineral Resources Statement April 14 2011 CARRAPATEENA MINERAL RESOURCE STATEMENT April 14, 2011 The Carrapateena Resource Statement relates to an upgrading to an Inferred
More informationADDITIVITY, METALLURGICAL RECOVERY, AND GRADE
ADDITIVITY, METALLURGICAL RECOVERY, AND GRADE PEDRO CARRASCO 1, JEAN-PAUL CHILÈS 2 and SERGE SÉGURET 2 1 Gerencia Técnica, Vicepresidencia de Proyectos, Codelco, Chile. 2 École des Mines de Paris, Centre
More informationAcceptable Ergodic Fluctuations and Simulation of Skewed Distributions
Acceptable Ergodic Fluctuations and Simulation of Skewed Distributions Oy Leuangthong, Jason McLennan and Clayton V. Deutsch Centre for Computational Geostatistics Department of Civil & Environmental Engineering
More informationGeostatistical Determination of Production Uncertainty: Application to Pogo Gold Project
Geostatistical Determination of Production Uncertainty: Application to Pogo Gold Project Jason A. McLennan 1, Clayton V. Deutsch 1, Jack DiMarchi 2 and Peter Rolley 2 1 University of Alberta 2 Teck Cominco
More informationSampling of variables with proportional effect
Sampling of variables with proportional effect Samuel Canchaya, R&D Senior Geologist, Cía. de Minas Buenaventura SAA, Lima, Peru, samuel.canchaya@buenaventura.pe ABSTRACT According to the theory of regionalized
More informationLocalized uniform conditioning (LUC): method and application case studies
Localized uniform conditioning (LUC): method and application case studies by M.Z. Abzalov* Synopsis A new method, localized uniform conditioning (LUC), was proposed in 2006 for modelling grades of small
More informationGeostatistics for Gaussian processes
Introduction Geostatistical Model Covariance structure Cokriging Conclusion Geostatistics for Gaussian processes Hans Wackernagel Geostatistics group MINES ParisTech http://hans.wackernagel.free.fr Kernels
More informationMultiple-Point Geostatistics: from Theory to Practice Sebastien Strebelle 1
Multiple-Point Geostatistics: from Theory to Practice Sebastien Strebelle 1 Abstract The limitations of variogram-based simulation programs to model complex, yet fairly common, geological elements, e.g.
More informationPRODUCING PROBABILITY MAPS TO ASSESS RISK OF EXCEEDING CRITICAL THRESHOLD VALUE OF SOIL EC USING GEOSTATISTICAL APPROACH
PRODUCING PROBABILITY MAPS TO ASSESS RISK OF EXCEEDING CRITICAL THRESHOLD VALUE OF SOIL EC USING GEOSTATISTICAL APPROACH SURESH TRIPATHI Geostatistical Society of India Assumptions and Geostatistical Variogram
More informationCharacterizing the mineralogical variability of a Chilean copper deposit using plurigaussian simulations
Characterizing the mineralogical variability of a Chilean copper deposit using plurigaussian simulations by J. Betzhold*, and C. Roth Synopsis Knowing more about an orebody makes it easier to exploit it
More informationCOLLOCATED CO-SIMULATION USING PROBABILITY AGGREGATION
COLLOCATED CO-SIMULATION USING PROBABILITY AGGREGATION G. MARIETHOZ, PH. RENARD, R. FROIDEVAUX 2. CHYN, University of Neuchâtel, rue Emile Argand, CH - 2009 Neuchâtel, Switzerland 2 FSS Consultants, 9,
More informationNews Release February 27, 2014 TSX SYMBOL: COP
Suite 1280 625 Howe St Vancouver, B.C. V6C 2T6 February 27, 2014 TSX SYMBOL: COP www.coromining.com CORO ANNOUNCES DRILLING RESULTS FROM ITS EL DESESPERADO PROJECT, CHILE February 27 2014, Coro Mining
More informationDISCOVERY OF MINERALISED PORPHYRY & MAGNETITE-COPPER-GOLD AT KAMARANGAN
MEDUSA MINING LIMITED ABN: 60 099 377 849 Unit 7, 11 Preston Street Como WA 6152 PO Box 860 Canning Bridge WA 6153 Telephone: +618-9367 0601 Facsimile: +618-9367 0602 Email: admin@medusamining.com.au Internet:
More informationEstimation of direction of increase of gold mineralisation using pair-copulas
22nd International Congress on Modelling and Simulation, Hobart, Tasmania, Australia, 3 to 8 December 2017 mssanz.org.au/modsim2017 Estimation of direction of increase of gold mineralisation using pair-copulas
More informationIdentifying and Dealing With Outliers in Resource Estimation
Identifying and Dealing With Outliers in Resource Estimation Ninth International Mine Geology Conference 2014 Chris De-Vitry Overview Aim What is an Outlier? What Causes an Outlier? Dealing with outliers
More informationCOLLOCATED CO-SIMULATION USING PROBABILITY AGGREGATION
COLLOCATED CO-SIMULATION USING PROBABILITY AGGREGATION G. MARIETHOZ, PH. RENARD, R. FROIDEVAUX 2. CHYN, University of Neuchâtel, rue Emile Argand, CH - 2009 Neuchâtel, Switzerland 2 FSS Consultants, 9,
More informationAzerbaijan International Mining Company Limited
Updated Mineral Resources Gedabek Mineral Deposit, Republic of Azerbaijan Azerbaijan International Mining Company Limited Prepared by CAE Mining CAE Mining 8585 Cote-de-Liesse Saint-Laurent Quebec H4T
More informationSUMMARY HIGHL. The project is located close to road and gas pipeline infrastructure and a large service centre.
INDEPENDENCE GROUP NL COMPANY DETAILS A SX A N NOUNCEMENT 2 1 s t JANUARY 2008 5 Pages IGO Acquires the Karlawinda Gold Project ASX CODE: IGO ABN: 46 092 786 304 DIRECTORS Rod Marston Non-Exec Chairman
More informationConditional Distribution Fitting of High Dimensional Stationary Data
Conditional Distribution Fitting of High Dimensional Stationary Data Miguel Cuba and Oy Leuangthong The second order stationary assumption implies the spatial variability defined by the variogram is constant
More informationA Case for Geometric Criteria in Resources and Reserves Classification
A Case for Geometric Criteria in Resources and Reserves Classification Clayton V. Deutsch 1, Oy Leuangthong 1 and Julián Ortiz C. 2 1 Centre for Computational Geostatistics (CCG) University of Alberta
More informationGeostatistics for Seismic Data Integration in Earth Models
2003 Distinguished Instructor Short Course Distinguished Instructor Series, No. 6 sponsored by the Society of Exploration Geophysicists European Association of Geoscientists & Engineers SUB Gottingen 7
More informationAn Introduction to Pattern Statistics
An Introduction to Pattern Statistics Nearest Neighbors The CSR hypothesis Clark/Evans and modification Cuzick and Edwards and controls All events k function Weighted k function Comparative k functions
More informationAdvanced analysis and modelling tools for spatial environmental data. Case study: indoor radon data in Switzerland
EnviroInfo 2004 (Geneva) Sh@ring EnviroInfo 2004 Advanced analysis and modelling tools for spatial environmental data. Case study: indoor radon data in Switzerland Mikhail Kanevski 1, Michel Maignan 1
More informationSummary of Rover Metals Geologic Mapping Program at the Up Town Gold Project, Northwest Territories
October 13, 2017 Summary of Rover Metals Geologic Mapping Program at the Up Town Gold Project, Northwest Territories Vancouver, British Columbia, Canada - Rover Metals Corp., Rover, is pleased to provide
More informationAPPLICATION OF LOCALISED UNIFORM CONDITIONING ON TWO HYPOTHETICAL DATASETS
APPLICATION OF LOCALISED UNIFORM CONDITIONING ON TWO HYPOTHETICAL DATASETS Kathleen Marion Hansmann A dissertation submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand,
More informationTransiogram: A spatial relationship measure for categorical data
International Journal of Geographical Information Science Vol. 20, No. 6, July 2006, 693 699 Technical Note Transiogram: A spatial relationship measure for categorical data WEIDONG LI* Department of Geography,
More informationManuscript of paper for APCOM 2003.
1 Manuscript of paper for APCOM 2003. AN ANALYSIS OF THE PRACTICAL AND ECONOMIC IMPLICATIONS OF SYSTEMATIC UNDERGROUND DRILLING IN DEEP SOUTH AFRICAN GOLD MINES W. ASSIBEY-BONSU Consultant: Geostatistics
More informationSINGULARITY ANALYSIS FOR IMAGE PROCESSING AND ANOMALY ENHANCEMENT. Qiuming CHENG
SINGULARITY ANALYSIS FOR IMAGE PROCESSING AND ANOMALY ENHANCEMENT Qiuming CHENG Department of Earth and Atmospheric Science, Department of Geography, York University, Toronto, 4700 Keele Street, Ont. M3J
More informationInverting hydraulic heads in an alluvial aquifer constrained with ERT data through MPS and PPM: a case study
Inverting hydraulic heads in an alluvial aquifer constrained with ERT data through MPS and PPM: a case study Hermans T. 1, Scheidt C. 2, Caers J. 2, Nguyen F. 1 1 University of Liege, Applied Geophysics
More informationD N HARLEY MANAGING DIRECTOR Attachment: Amended Consultant Report on MG 14 Mineral Resource Estimation.
ABN 32 090 603 642 ASX RELEASE 11 June 2013 AMENDED 2012 JORC REPORT TO ACCOMPANY 6 JUNE 2013 COMPANY UPDATE Following its review of the Company s announcement of 6 June 2013, ASX requested some modifications
More informationGeostatistical applications in petroleum reservoir modelling
Geostatistical applications in petroleum reservoir modelling by R. Cao*, Y. Zee Ma and E. Gomez Synopsis Geostatistics was initially developed in the mining sector, but has been extended to other geoscience
More informationThe use of Conditional Simulation for Drill Hole Spacing Evaluation and Decision-Making in Telégrafo Project, Northern Chile
The use of Conditional Simulation for Drill Hole Spacing Evaluation and Decision-Making in Telégrafo Project, Northern Chile O Rojas 1 and A Caceres 2 ABSTRACT The Telégrafo copper gold deposit is located
More informationQuantifying uncertainty of geological 3D layer models, constructed with a-priori
Quantifying uncertainty of geological 3D layer models, constructed with a-priori geological expertise Jan Gunnink, Denise Maljers 2 and Jan Hummelman 2, TNO Built Environment and Geosciences Geological
More information3D model of a Supergene Gold Deposit derived from Spectral Mapping
3D model of a Supergene Gold Deposit derived from Spectral Mapping Exploration Technologies 2011 Scott Halley ABSTRACT: A 3D MODEL OF A SUPERGENE GOLD DEPOSIT CREATED FROM SPECTRALLY DERIVED MINERALOGY
More informationFinding the Nearest Positive Definite Matrix for Input to Semiautomatic Variogram Fitting (varfit_lmc)
Finding the Nearest Positive Definite Matrix for Input to Semiautomatic Variogram Fitting (varfit_lmc) Arja Jewbali (arja.jewbali@riotinto.com) Resource Estimation Geologist Rio Tinto Iron Ore In resource
More informationAustralia s Response To The Chile Technological Roadmap In Mining : The University of Queensland Experience
Australia s Response To The Chile Technological Roadmap In Mining : The University of Queensland Experience CHALLENGE 1: Underground mining: Development of large-scale deep mining Professor and Chair In
More informationInvestigation of Monthly Pan Evaporation in Turkey with Geostatistical Technique
Investigation of Monthly Pan Evaporation in Turkey with Geostatistical Technique Hatice Çitakoğlu 1, Murat Çobaner 1, Tefaruk Haktanir 1, 1 Department of Civil Engineering, Erciyes University, Kayseri,
More informationAnomaly Density Estimation from Strip Transect Data: Pueblo of Isleta Example
Anomaly Density Estimation from Strip Transect Data: Pueblo of Isleta Example Sean A. McKenna, Sandia National Laboratories Brent Pulsipher, Pacific Northwest National Laboratory May 5 Distribution Statement
More informationFormats for Expressing Acceptable Uncertainty
Formats for Expressing Acceptable Uncertainty Brandon J. Wilde and Clayton V. Deutsch This short note aims to define a number of formats that could be used to express acceptable uncertainty. These formats
More informationDrill locations for the 2015 program are highlighted in the geology map below.
2015 Exploration Program The exploration program plan at KSM for 2015 was designed to improve the understanding of block cave targets and support engineering/environmental aspects of development scenarios.
More informationCOMMANDER RESOURCES LTD.
COMMANDER RESOURCES LTD. October Dome Gold Skarn Mt Polley Core Facility February 2016 1 August 2016 CORPORATE DISCLOSURE Disclaimer The information contained herein, while obtained from sources which
More informationToward an automatic real-time mapping system for radiation hazards
Toward an automatic real-time mapping system for radiation hazards Paul H. Hiemstra 1, Edzer J. Pebesma 2, Chris J.W. Twenhöfel 3, Gerard B.M. Heuvelink 4 1 Faculty of Geosciences / University of Utrecht
More informationStatistical Evaluations in Exploration for Mineral Deposits
Friedrich-Wilhelm Wellmer Statistical Evaluations in Exploration for Mineral Deposits Translated by D. Large With 120 Figures and 74 Tables Springer Preface The Most Important Notations and Abbreviations
More informationSpatial representativeness of an air quality monitoring station. Application to NO2 in urban areas
Spatial representativeness of an air quality monitoring station. Application to NO2 in urban areas Maxime Beauchamp, Laure Malherbe, Laurent Letinois, Chantal De Fouquet To cite this version: Maxime Beauchamp,
More informationBuilding Blocks for Direct Sequential Simulation on Unstructured Grids
Building Blocks for Direct Sequential Simulation on Unstructured Grids Abstract M. J. Pyrcz (mpyrcz@ualberta.ca) and C. V. Deutsch (cdeutsch@ualberta.ca) University of Alberta, Edmonton, Alberta, CANADA
More informationDownhole Molybdenum Grade Distribution of the Red Hills Mo- Cu deposit, Trans-Pecos Texas
2013 Downhole Molybdenum Grade Distribution of the Red Hills Mo- Cu deposit, Trans-Pecos Texas Stefanie Frelinger University of Texas at Austin, Jackson School of Geosciences GEO386G: GIS and GPS Applications
More informationGeostatistical approaches on the thermal conductivities of rocks
H: Workshop Geothermal Studies: Instruments, Measurements, and Interpretation Geostatistical approaches on the thermal conductivities of rocks Byoung Ohan Shim, Jeongmin Park, Hyoung Chan Kim, Youngmin
More informationWe Prediction of Geological Characteristic Using Gaussian Mixture Model
We-07-06 Prediction of Geological Characteristic Using Gaussian Mixture Model L. Li* (BGP,CNPC), Z.H. Wan (BGP,CNPC), S.F. Zhan (BGP,CNPC), C.F. Tao (BGP,CNPC) & X.H. Ran (BGP,CNPC) SUMMARY The multi-attribute
More informationResource classification in coal
Resource classification in coal It s time to stop going around in circles mdgeology.com.au Why do we classify resources? Required for reporting resources in accordance with the requirements of the JORC
More informationNew Drilling Program Commences at Mutiny s Deflector Deposit
New Drilling Program Commences at Mutiny s Deflector Deposit Drilling Program Highlights 12,000 metre RC and diamond drilling program underway New drilling program targets strike extensions in the Northern
More informationABSTRACT INTRODUCTION
SGS MINERALS SERVICES TECHNICAL BULLETIN 2010-1 JANUARY 2010 VISION FOR A RISK ADVERSE INTEGRATED GEOMETALLURGY FRAMEWORK GUILLERMO TURNER-SAAD, GLOBAL VICE PRESIDENT METALLURGY AND MINERALOGY, SGS MINERAL
More informationLINGIG PORPHYRY COPPER DISCOVERY
MEDUSA MINING LIMITED ABN: 60 099 377 849 Unit 7, 11 Preston Street Como WA 6152 PO Box 860 Canning Bridge WA 6153 Telephone: 618-9367 0601 Facsimile: 618-9367 0602 Email: admin@medusamining.com.au Internet:
More informationComputational Challenges in Reservoir Modeling. Sanjay Srinivasan The Pennsylvania State University
Computational Challenges in Reservoir Modeling Sanjay Srinivasan The Pennsylvania State University Well Data 3D view of well paths Inspired by an offshore development 4 platforms 2 vertical wells 2 deviated
More informationA Short Note on the Proportional Effect and Direct Sequential Simulation
A Short Note on the Proportional Effect and Direct Sequential Simulation Abstract B. Oz (boz@ualberta.ca) and C. V. Deutsch (cdeutsch@ualberta.ca) University of Alberta, Edmonton, Alberta, CANADA Direct
More informationApplicability of Near-Infrared (NIR) spectroscopy for sensor based sorting of a porphyry copper ore.
Applicability of Near-Infrared (NIR) spectroscopy for sensor based sorting of a porphyry copper ore. OCM conference presentation 07-03-2013 TU Delft, Resource Engineering section M.Dalm MSc. 1 Table of
More informationGEOSTATISTICAL SIMULATION TECHNIQUES APPLIED TO KIMBERLITE OREBODIES AND RISK ASSESSMENT OF SAMPLING STRATEGIES
GEOSTATISTICAL SIMULATION TECHNIQUES APPLIED TO KIMBERLITE OREBODIES AND RISK ASSESSMENT OF SAMPLING STRATEGIES JACQUES DERAISME 1 and DAVID FARROW 2 1 Geovariances, 49 Ave Franklin Roosevelt, Avon, 77212
More informationMultivariate geostatistical simulation of the Gole Gohar iron ore deposit, Iran
http://dx.doi.org/10.17159/2411-9717/2016/v116n5a8 Multivariate geostatistical simulation of the Gole Gohar iron ore deposit, Iran by S.A. Hosseini* and O. Asghari* The quantification of mineral resources
More informationModelling Ore Bodies of High-Nugget Gold Using Conditional Probability
Modelling Ore Bodies of High-Nugget Gold Using Conditional Probability Evelyn June Hill, Nicholas H. S. Oliver, James Cleverley and Michael Nugus 1 Introduction In vein-hosted gold deposits, gold distribution
More informationSPL 1452 NAMOLI & WAINIVAU GOLD AND COPPER PROSPECTS
SPL 1452 NAMOLI & WAINIVAU GOLD AND COPPER PROSPECTS (Multiple Namosi-like Exploration Targets) SPL1452 being contiguous with the Namosi SPL1420 owned by the Newcrest Joint Venture and with the same type
More informationGeostatistical characterisation of contaminated metals: methodology and illustrations
1 Geostatistical characterisation of contaminated metals: methodology and illustrations Per Lidar and Arne Larsson, Studsvik Nuclear AB Yvon Desnoyers, Geovariances Symp. on Recycling of Metals arising
More informationBAYESIAN MODEL FOR SPATIAL DEPENDANCE AND PREDICTION OF TUBERCULOSIS
BAYESIAN MODEL FOR SPATIAL DEPENDANCE AND PREDICTION OF TUBERCULOSIS Srinivasan R and Venkatesan P Dept. of Statistics, National Institute for Research Tuberculosis, (Indian Council of Medical Research),
More informationDiscovery of Thick Zone of Magnetite-Rich M Veins Strengthens Potential for Nearby Porphyry
Thursday s Gossan Copper-Gold Porphyry Diamond Drilling Update Discovery of Thick Zone of Magnetite-Rich M Veins Strengthens Potential for Nearby Porphyry ~100m intercept of magnetite-rich M -veins and
More informationStatistical Tools and Concepts
Statistical Tools and Concepts Abstract Mineral resource estimation requires extensive use of statistics. In our context, statistics are mathematical methods for collecting, organizing, and interpreting
More informationAdvances in Locally Varying Anisotropy With MDS
Paper 102, CCG Annual Report 11, 2009 ( 2009) Advances in Locally Varying Anisotropy With MDS J.B. Boisvert and C. V. Deutsch Often, geology displays non-linear features such as veins, channels or folds/faults
More informationSpatial Analysis 1. Introduction
Spatial Analysis 1 Introduction Geo-referenced Data (not any data) x, y coordinates (e.g., lat., long.) ------------------------------------------------------ - Table of Data: Obs. # x y Variables -------------------------------------
More informationReliability of Seismic Data for Hydrocarbon Reservoir Characterization
Reliability of Seismic Data for Hydrocarbon Reservoir Characterization Geetartha Dutta (gdutta@stanford.edu) December 10, 2015 Abstract Seismic data helps in better characterization of hydrocarbon reservoirs.
More informationVisual comparison of Moving Window Kriging models
Urška Demšar & Paul Harris Visual comparison of Moving Window Kriging models Dr Urška Demšar National Centre for Geocomputation National University of Ireland Maynooth urska.demsar@nuim.ie Work supported
More informationOYUT ULAAN COPPER GOLD PROJECT RECONNAISSANCE DRILLING RESULTS
ASX / MEDIA RELEASE Manager Company Announcements By Electronic Lodgement 7 pages Company Announcements Office 6 June 2013 Australian Securities Exchange 4th Floor, 20 Bridge Street SYDNEY NSW 2001 OYUT
More informationFor personal use only
South America s Emerging Gold Explorer ASX Release 22 November 2012 OUTSTANDING GEOPHYSICS RESULTS FROM ALUMBRE AND QUINUAL CONCESSIONS HIGHLIGHTS Independent review of the Alumbre and Quinual concessions
More informationAnomaly Density Mapping: Lowry AGGR Site Demonstration Report
Environmental Security Technology Certification Program ESTCP Anomaly Density Mapping: Lowry AGGR Site Demonstration Report ESTCP Project # 200325 Final Report 09/30/2006 Sean A. McKenna Sandia National
More informationUniversity of Cambridge. MPhil in Computer Speech Text & Internet Technology. Module: Speech Processing II. Lecture 2: Hidden Markov Models I
University of Cambridge MPhil in Computer Speech Text & Internet Technology Module: Speech Processing II Lecture 2: Hidden Markov Models I o o o o o 1 2 3 4 T 1 b 2 () a 12 2 a 3 a 4 5 34 a 23 b () b ()
More informationStatistícal Methods for Spatial Data Analysis
Texts in Statistícal Science Statistícal Methods for Spatial Data Analysis V- Oliver Schabenberger Carol A. Gotway PCT CHAPMAN & K Contents Preface xv 1 Introduction 1 1.1 The Need for Spatial Analysis
More informationConcepts and Applications of Kriging. Eric Krause
Concepts and Applications of Kriging Eric Krause Sessions of note Tuesday ArcGIS Geostatistical Analyst - An Introduction 8:30-9:45 Room 14 A Concepts and Applications of Kriging 10:15-11:30 Room 15 A
More informationStepwise Conditional Transformation in Estimation Mode. Clayton V. Deutsch
Stepwise Conditional Transformation in Estimation Mode Clayton V. Deutsch Centre for Computational Geostatistics (CCG) Department of Civil and Environmental Engineering University of Alberta Stepwise Conditional
More information