Incorporating conceptual and interpretation uncertainty to mineral prospectivity modelling
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摘要
Prospectivity analyses are used to reduce the exploration search space for locating areas prospective for mineral deposits.The scale of a study and the type of mineral system associated with the deposit control the evidence layers used as proxies that represent critical ore genesis processes.In particular,knowledge-driven approaches(fuzzy logic)use a conceptual mineral systems model from which data proxies represent the critical components.These typically vary based on the scale of study and the type of mineral system being predicted.Prospectivity analyses utilising interpreted data to represent proxies for a mineral system model inherit the subjectivity of the interpretations and the uncertainties of the evidence layers used in the model.In the case study presented,the prospectivity for remobilised nickel sulphide(NiS)in the west Kimberley,Western Australia,is assessed with two novel techniques that objectively grade interpretations and accommodate alternative mineralisation scenarios.Exploration targets are then identified and supplied with a robustness assessment that reflects the variability of prospectivity value for each location when all models are considered.The first technique grades the strength of structural interpretations on an individual line-segment basis.Gradings are obtained from an objective measure of feature evidence,which is the quantification of specific patterns in geophysical data that are considered to reveal underlying structure.Individual structures are weighted in the prospectivity model with grading values correlated to their feature evidence.This technique allows interpreted features to contribute prospectivity proportional to their strength in feature evidence and indicates the level of associated stochastic uncertainty.The second technique aims to embrace the systemic uncertainty of modelling complex mineral systems.In this approach,multiple prospectivity maps are each generated with different combinations of confidence values applied to evidence layers to represent the diversity of processes potentially leading to ore deposition.With a suite of prospectivity maps,the most robust exploration targets are the locations with the highest prospectivity values showing the smallest range amongst the model suite.This new technique offers an approach that reveals to the modeller a range of alternative mineralisation scenarios while employing a sensible mineral systems model,robust modelling of prospectivity and significantly reducing the exploration search space for Ni.
Prospectivity analyses are used to reduce the exploration search space for locating areas prospective for mineral deposits.The scale of a study and the type of mineral system associated with the deposit control the evidence layers used as proxies that represent critical ore genesis processes.In particular,knowledge-driven approaches(fuzzy logic)use a conceptual mineral systems model from which data proxies represent the critical components.These typically vary based on the scale of study and the type of mineral system being predicted.Prospectivity analyses utilising interpreted data to represent proxies for a mineral system model inherit the subjectivity of the interpretations and the uncertainties of the evidence layers used in the model.In the case study presented,the prospectivity for remobilised nickel sulphide(NiS)in the west Kimberley,Western Australia,is assessed with two novel techniques that objectively grade interpretations and accommodate alternative mineralisation scenarios.Exploration targets are then identified and supplied with a robustness assessment that reflects the variability of prospectivity value for each location when all models are considered.The first technique grades the strength of structural interpretations on an individual line-segment basis.Gradings are obtained from an objective measure of feature evidence,which is the quantification of specific patterns in geophysical data that are considered to reveal underlying structure.Individual structures are weighted in the prospectivity model with grading values correlated to their feature evidence.This technique allows interpreted features to contribute prospectivity proportional to their strength in feature evidence and indicates the level of associated stochastic uncertainty.The second technique aims to embrace the systemic uncertainty of modelling complex mineral systems.In this approach,multiple prospectivity maps are each generated with different combinations of confidence values applied to evidence layers to represent the diversity of processes potentially leading to ore deposition.With a suite of prospectivity maps,the most robust exploration targets are the locations with the highest prospectivity values showing the smallest range amongst the model suite.This new technique offers an approach that reveals to the modeller a range of alternative mineralisation scenarios while employing a sensible mineral systems model,robust modelling of prospectivity and significantly reducing the exploration search space for Ni.
Prospectivity analyses are used to reduce the exploration search space for locating areas prospective for mineral deposits.The scale of a study and the type of mineral system associated with the deposit control the evidence layers used as proxies that represent critical ore genesis processes.In particular,knowledge-driven approaches(fuzzy logic)use a conceptual mineral systems model from which data proxies represent the critical components.These typically vary based on the scale of study and the type of mineral system being predicted.Prospectivity analyses utilising interpreted data to represent proxies for a mineral system model inherit the subjectivity of the interpretations and the uncertainties of the evidence layers used in the model.In the case study presented,the prospectivity for remobilised nickel sulphide(NiS)in the west Kimberley,Western Australia,is assessed with two novel techniques that objectively grade interpretations and accommodate alternative mineralisation scenarios.Exploration targets are then identified and supplied with a robustness assessment that reflects the variability of prospectivity value for each location when all models are considered.The first technique grades the strength of structural interpretations on an individual line-segment basis.Gradings are obtained from an objective measure of feature evidence,which is the quantification of specific patterns in geophysical data that are considered to reveal underlying structure.Individual structures are weighted in the prospectivity model with grading values correlated to their feature evidence.This technique allows interpreted features to contribute prospectivity proportional to their strength in feature evidence and indicates the level of associated stochastic uncertainty.The second technique aims to embrace the systemic uncertainty of modelling complex mineral systems.In this approach,multiple prospectivity maps are each generated with different combinations of confidence values applied to evidence layers to represent the diversity of processes potentially leading to ore deposition.With a suite of prospectivity maps,the most robust exploration targets are the locations with the highest prospectivity values showing the smallest range amongst the model suite.This new technique offers an approach that reveals to the modeller a range of alternative mineralisation scenarios while employing a sensible mineral systems model,robust modelling of prospectivity and significantly reducing the exploration search space for Ni.
引文
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Barnes,S.J.,Lightfoot,P.C.,2005.Formation of magmatic nickel-sulfide ore deposits and processes affecting their copper and platinum-group element contents.Economic Geology One Hundredth Anniversary Volume 179-213.
Bonham-Carter,G.F.,1994.Geographic Information Systems for Geoscientists:Modelling with GIS.Pergamon Press,Oxford.
Buxton Resources,2016.ASX Announcement(04/12/16):PGE Results Support Magmatic Genesis at Double Magic Project,p.7.
Buxton Res ources,2017.ASX Announcement(20/09/17):Double Magic Ni-Cu Project Exploration Update,p.11.
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Carranza,.J.M.,2009.Geodiemical Anomaly and Mineral Prospectivity Mapping in GIS.Elsevier,Amsterdam.
Carranza,E.J.M.,2015.Data-driven evidential belief modeling of mineral potential using few prospects and evidence with missing values.Natural Resources Re search 24.https://doi.org/10.1007/s11053-014-9250-z.
Carranza,E.J.M.,Hale,M.,Faassen,C.,2008.Selection of coherent deposit-type locations and their application in data-driven mineral prospectivity mapping.Ore Geology Reviews 33(3-4),536-558.
Cave,M.R,Wood,B,2002.Approaches to the Measurement of Uncertainty in Geoscience Data Modelling.British Geological Survey(Internal)Report,IR/02/068.
Chudasama,B.,Porwal.A.,Kreuzer,O.P.,Bute ra,K.,2016.Geology,geodynamics a nd orogenic gold prospectivity modelling of the Paleoproterozoic Kumasi Basin,Ghana,West Africa.Ore Geology Reviews 78,692-711.
Cox,D.P.,Singe r,D.A.,1992.Grade and tonnage model of distal disseminated Ag-Au.In:Bliss,J.D.(Ed.),Developments in Mineral Deposit Modeling:U.S.Geological Survey Bulletin 2004,20-22.
Dentith,M.,Mudge,S.T.,2014.Geophysics for the Mineral Exploration Gcoscicntist.Cambridge University Press.,London,p.516.
Duuring,P.,Bleeker,W.,Beresford,S.W.,2007.Structural modification of the komatiite-associated Harmony nickel sulfide deposit,Leinster,Western Australia.Economic Geology 102,277-297.
Ford,A.,Hart,C.J.R.,2013.Mineral potential mapping in frontier regions:a Mongolian case study.Ore Geology Reviews 51,15-26.
Goldfarb,R.,Groves,D.,2015.Orogenic gold:common or evolving fluid and me tal sources through time.Lithos 233,2-26.https://doi.org/10.1016/j.lithos.2015.07.011.
Griffin,T.J.,Tyler.I.M.,Playford,P.E.,Lewis,J.D.,1993.Lennard River,Western Australia,1:250 000 Geological Series Explanatory Notes,Sheet SE51-8,third ed.Geological Survey of Western Australia 1v,p.56.
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Gonzalez-Alvarez,I.,Sweetapple,M.,Lindley,I.D.,Kirakar,J.,2013b.Hydrothermal Ni:Doriri Creek,Papua New Guinea.Ore Geology Reviews 52,37-57.
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Holden,E.J., Wong,J.C.,Wedge,D.,Martis,M.,Lindsay,M.,Gessner,K.,2016.Improving assessment of geological structure interpretation of magnetic data:an advanced data analytics approach.Computers&Geosciences 87,101-111.
Hronsky,J.MA.,Groves,D.I.,2008.Science of targeting:definition,strategies,targeting and performance measurement.Australian Journal of Eanth Sciences 55(1),3-12.
Imam,R.L,Helton,J.C.,1988.An investigation into uncertainty and sensitivity analysis techniques for computer models.Risk Analysis 8(1),71-90.
Isles,D.J.,Rankin,LR.,2013.Geological Interpretation of Aeromagnetic Data.CSIRO Publishing.
Joly,A.,Porwal,A.,McCuaig,T.C.,2012.Exploration targeting for orogenic gold deposits in the Granites-Tanami Orogen:mineral system analysis,targeting model and prospectivity analysis.Ore Geology Reviews 48,349-383.
Joly,A.,Porwal,A.,McCuaig,T.C.,Chudasama,B.,Dentith,M.C.,Aitken,A.R.A.,2015.Mineral systems approach applied to GIS-based 2D-prospectivity modelling of geological regions:insights from Western Australia.Ore Geology Reviews 71,673-702.
Jones,R.M.,Hillis,R.R.,2003.An integrated,quantitative approach to assessing fault-seal risk.AAPG Bulletin 87(3).507-524.
Jones,S.,Lacey,P.,Walshe,T.,2009.A dynamic hyd rological Monte Carlo simulation model to inform decision-making at Lake Toolibin,Western Australia.Journal of Environmental Manageme nt 90(5),1761-1769.
Knox-Robinson,C.M.,Wyborn,LA.L.,1997.Towards a holistic exploration strategy:using geographic information systems as a tool to enhance exploration.Australian Journal of Earth Sciences 44(4),453-463.
Kovesi,P.,1997.Symmetry and asymmetry from local phase.Proceedings of the Tenth Australian Joint Conference on Artificial Intelligence 2-4.
Kovesi,P.,1999.Image features from phase congruency.Journal of Computer Vision Research 1,1-26.
Kovesi,P.,2012.Phase preserving tone mapping of non-photographic high dynamic range images.In:Proceedings 2012 International Conference on Digital Image Computing Techniques and Applications(DICTA)3-5 Dec.2012,pp.1-8.
Layton-Matthews,D.,Lesher,C.M.,Burnham,O.M.,Liwanag,J.,Halden,N.M.,Hulbert,L,Peck,D.C.,2007.Magmatic Ni-Cu-platinum-group ele ment deposits of the Thompson nickel Belt In:Goodfellow,W.D.(Ed.),Mineral Deposits of Canada:A Synthesis of Major Deposit-Type s,District Metallogeny,the Evolution of Geological Provinces,and Exploration Methods,vol.5.Geological Association of Canada Special Publication.
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Li,C, Ripley.,E,Naldrett,A.,2009.A new genetic model for the giant Ni-Cu-PGE sulfide deposits associated with the Siberian flood basalts.Economic Geology 104(2),291-301.
Lindsay,M.D.,Aillères,L,Jessell,M.W.,de Kemp,F.A., Betts,P.G.,2012.Locating and quantifying geological unce rtainty in three-dimensional models:analysis of the Gippsland Basin,southeastern Australia.Tectonophysics 546-547,10-27.https://doi.org/10.1016/j.tecto.2012.04.007.
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1 CET Grid Analysis extension:https://www.geosoft.com/products/oasis-montaj/extensions/cet-grid-analysis.
2 Available from https://www.waexplorationplatform.wa.edu.au/.
Barnes,S.J.,Cruden,A.R.,Nicholas,A.,Saumur,B.M.,2016.The mineral system approach applied to magmatic Ni-Cu-PGE sulphide deposits.Ore Geology Reviews 76,296-316.
Barnes,S.J.,Fiorentini,M.,2012.Komatiite magmas and sulfide nickel deposits:a comparis on of variably endowed archean terranes.Economic Geology 107(5),755-780.
Barnes,S.J.,Hill,R.E.T.,2000.Metamorphism of komatiite-hosted nickel sulfide deposits.Metamorphosed and Metamorphogenic Ore Deposits.In:Spry,P.G.,Marshall,B.,Vokes,F.M.(Ed s.).Reviews in Economic Geology,Chapter 9,vol.11,pp.203-215.
Barnes,S.J.,Lightfoot,P.C.,2005.Formation of magmatic nickel-sulfide ore deposits and processes affecting their copper and platinum-group element contents.Economic Geology One Hundredth Anniversary Volume 179-213.
Bonham-Carter,G.F.,1994.Geographic Information Systems for Geoscientists:Modelling with GIS.Pergamon Press,Oxford.
Buxton Resources,2016.ASX Announcement(04/12/16):PGE Results Support Magmatic Genesis at Double Magic Project,p.7.
Buxton Res ources,2017.ASX Announcement(20/09/17):Double Magic Ni-Cu Project Exploration Update,p.11.
Buxton Resources,2018.ASX Announce ment(31/01/18.Quarterly Activities Report,p.11.
Carranza,.J.M.,2009.Geodiemical Anomaly and Mineral Prospectivity Mapping in GIS.Elsevier,Amsterdam.
Carranza,E.J.M.,2015.Data-driven evidential belief modeling of mineral potential using few prospects and evidence with missing values.Natural Resources Re search 24.https://doi.org/10.1007/s11053-014-9250-z.
Carranza,E.J.M.,Hale,M.,Faassen,C.,2008.Selection of coherent deposit-type locations and their application in data-driven mineral prospectivity mapping.Ore Geology Reviews 33(3-4),536-558.
Cave,M.R,Wood,B,2002.Approaches to the Measurement of Uncertainty in Geoscience Data Modelling.British Geological Survey(Internal)Report,IR/02/068.
Chudasama,B.,Porwal.A.,Kreuzer,O.P.,Bute ra,K.,2016.Geology,geodynamics a nd orogenic gold prospectivity modelling of the Paleoproterozoic Kumasi Basin,Ghana,West Africa.Ore Geology Reviews 78,692-711.
Cox,D.P.,Singe r,D.A.,1992.Grade and tonnage model of distal disseminated Ag-Au.In:Bliss,J.D.(Ed.),Developments in Mineral Deposit Modeling:U.S.Geological Survey Bulletin 2004,20-22.
Dentith,M.,Mudge,S.T.,2014.Geophysics for the Mineral Exploration Gcoscicntist.Cambridge University Press.,London,p.516.
Duuring,P.,Bleeker,W.,Beresford,S.W.,2007.Structural modification of the komatiite-associated Harmony nickel sulfide deposit,Leinster,Western Australia.Economic Geology 102,277-297.
Ford,A.,Hart,C.J.R.,2013.Mineral potential mapping in frontier regions:a Mongolian case study.Ore Geology Reviews 51,15-26.
Goldfarb,R.,Groves,D.,2015.Orogenic gold:common or evolving fluid and me tal sources through time.Lithos 233,2-26.https://doi.org/10.1016/j.lithos.2015.07.011.
Griffin,T.J.,Tyler.I.M.,Playford,P.E.,Lewis,J.D.,1993.Lennard River,Western Australia,1:250 000 Geological Series Explanatory Notes,Sheet SE51-8,third ed.Geological Survey of Western Australia 1v,p.56.
Griffin,T.J.,Page,R.W.,Sheppard,S.,Tyler,I.M.,2000.Tectonic implications of Palaeoproterozoic post-collisional,high-K felsic igneous rocks from the Kimberley region of northwestern Australia.Precambrian Res earch 101(1),1-23.
Gonzalez-Alvarez,I.,Pirajno.F.,Kerrich,R.,2013a.Hydrothermal nickel deposits:secular variation and diversity.Ore Geology Reviews 52,1-3.
Gonzalez-Alvarez,I.,Sweetapple,M.,Lindley,I.D.,Kirakar,J.,2013b.Hydrothermal Ni:Doriri Creek,Papua New Guinea.Ore Geology Reviews 52,37-57.
GSWA,2017.Kimberley 2017:Geological Information Series.Available online at:http://dmpbookshop.eruditetechnologies.com.au/product/kimberley-201 7.d o.
Halton,J.H.,1970.A retrospective and prospective survey of the Monte Carlo method.SIAM Review 12(1),1-63.
Hassan,L.Y.,2004.Mineral Occurrences and Exploration Potential of the West Kimberley.Report 88.Western Australian Geological Survey,p.88.
Holden,E.J., Wong,J.C.,Wedge,D.,Martis,M.,Lindsay,M.,Gessner,K.,2016.Improving assessment of geological structure interpretation of magnetic data:an advanced data analytics approach.Computers&Geosciences 87,101-111.
Hronsky,J.MA.,Groves,D.I.,2008.Science of targeting:definition,strategies,targeting and performance measurement.Australian Journal of Eanth Sciences 55(1),3-12.
Imam,R.L,Helton,J.C.,1988.An investigation into uncertainty and sensitivity analysis techniques for computer models.Risk Analysis 8(1),71-90.
Isles,D.J.,Rankin,LR.,2013.Geological Interpretation of Aeromagnetic Data.CSIRO Publishing.
Joly,A.,Porwal,A.,McCuaig,T.C.,2012.Exploration targeting for orogenic gold deposits in the Granites-Tanami Orogen:mineral system analysis,targeting model and prospectivity analysis.Ore Geology Reviews 48,349-383.
Joly,A.,Porwal,A.,McCuaig,T.C.,Chudasama,B.,Dentith,M.C.,Aitken,A.R.A.,2015.Mineral systems approach applied to GIS-based 2D-prospectivity modelling of geological regions:insights from Western Australia.Ore Geology Reviews 71,673-702.
Jones,R.M.,Hillis,R.R.,2003.An integrated,quantitative approach to assessing fault-seal risk.AAPG Bulletin 87(3).507-524.
Jones,S.,Lacey,P.,Walshe,T.,2009.A dynamic hyd rological Monte Carlo simulation model to inform decision-making at Lake Toolibin,Western Australia.Journal of Environmental Manageme nt 90(5),1761-1769.
Knox-Robinson,C.M.,Wyborn,LA.L.,1997.Towards a holistic exploration strategy:using geographic information systems as a tool to enhance exploration.Australian Journal of Earth Sciences 44(4),453-463.
Kovesi,P.,1997.Symmetry and asymmetry from local phase.Proceedings of the Tenth Australian Joint Conference on Artificial Intelligence 2-4.
Kovesi,P.,1999.Image features from phase congruency.Journal of Computer Vision Research 1,1-26.
Kovesi,P.,2012.Phase preserving tone mapping of non-photographic high dynamic range images.In:Proceedings 2012 International Conference on Digital Image Computing Techniques and Applications(DICTA)3-5 Dec.2012,pp.1-8.
Layton-Matthews,D.,Lesher,C.M.,Burnham,O.M.,Liwanag,J.,Halden,N.M.,Hulbert,L,Peck,D.C.,2007.Magmatic Ni-Cu-platinum-group ele ment deposits of the Thompson nickel Belt In:Goodfellow,W.D.(Ed.),Mineral Deposits of Canada:A Synthesis of Major Deposit-Type s,District Metallogeny,the Evolution of Geological Provinces,and Exploration Methods,vol.5.Geological Association of Canada Special Publication.
Le Vaillant,M.,Barnes,S.J.,Fiorentini,M.,Mole,D.,Austin,J.R.,Godel,B.,Patters on,B.,Hammcrli,J.,LaFlammc,C.,Ncaud,A.,Dcnyszyn,S., Mao.Y.-J.,2017.Magmatic Sulfide Mineral Potential in the East Kimberley.MRIWA project M#459-M#484,p.297.
Li,C, Ripley.,E,Naldrett,A.,2009.A new genetic model for the giant Ni-Cu-PGE sulfide deposits associated with the Siberian flood basalts.Economic Geology 104(2),291-301.
Lindsay,M.D.,Aillères,L,Jessell,M.W.,de Kemp,F.A., Betts,P.G.,2012.Locating and quantifying geological unce rtainty in three-dimensional models:analysis of the Gippsland Basin,southeastern Australia.Tectonophysics 546-547,10-27.https://doi.org/10.1016/j.tecto.2012.04.007.
Lindsay,M.D.,Betts,P.G.,Ailleres,L,2014.Data fusion and porphyry copper prospectivity models.Southeastern Arizona:Ore Geology Reviews 61,120-140.https://d oi.org/10.1016/j.oregeorev.2014.02.001.
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1 CET Grid Analysis extension:https://www.geosoft.com/products/oasis-montaj/extensions/cet-grid-analysis.
2 Available from https://www.waexplorationplatform.wa.edu.au/.