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 ev...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.展开更多
The cratonisation of Western Australia during the Proterozoic overlapped with several key events in the evolution of Earth. These include global oxidation events and glaciations, as well as the assembly,accretionary g...The cratonisation of Western Australia during the Proterozoic overlapped with several key events in the evolution of Earth. These include global oxidation events and glaciations, as well as the assembly,accretionary growth, and breakup of the supercontinents Columbia and Rodinia, culminating in the assembly of Gondwana. Globally, Proterozoic mineral systems evolved in response to the coupled evolution of the atmosphere, hydrosphere, biosphere and lithosphere. Consequently, mineral deposits form preferentially in certain times, but they also require a favourable tectonic setting. For Western Australia a distinct plate-margin mineralisation trend is associated with Columbia, whereas an intraplate mineralisation trend is associated with Rodinia and Gondwana, each with associated deposit types. We compare the current Proterozoic record of ore deposits in Western Australia to the estimated likelihood of oredeposit formation. Overall likelihood is estimated with a simple matrix-based approach that considers two components: The "global secular likelihood" and the "tectonic setting likelihood". This comparative study shows that at least for the studied ore-deposit types, deposits within Western Australia developed at times, and in tectonic settings compatible with global databases. Nevertheless, several deposit types are either absent or poorly-represented relative to the overall likelihood models. Insufficient exploration may partly explain this, but a genuine lack of deposits is also suggested for some deposit types. This may relate either to systemic inadequacies that inhibited ore-deposit formation, or to poor preservation. The systematic understanding on the record of Western Australia helps to understand mineralisation processes within Western Australia and its past connections in Columbia, Rodinia and Gondwana and aids to identify regions of high exploration potential.展开更多
Geologically representative feature engineering is a crucial component in geoscientific applications of machine learning.Many commonly applied feature engineering techniques used to produce input variables for machine...Geologically representative feature engineering is a crucial component in geoscientific applications of machine learning.Many commonly applied feature engineering techniques used to produce input variables for machine learning apply geological knowledge to generic data science techniques,which can lead to ambiguity,geological oversimplification,and/or compounding subjective bias.Workflows that utilize minimally processed input variables attempt to overcome these issues,but often lead to convoluted and uninterpretable results.To address these challenges,new and enhanced feature engineering methods were developed by combining geological knowledge,understanding of data limitations,and a variety of data science techniques.These include non-Euclidean fluid pre-deformation path distance,rheological and chemical contrast,geologically constrained interpolation of characteristic host rock geochemistry,interpolation of mobile element gain/loss,assemblages,magnetic intensity,structural complexity,host rock physical properties.These methods were applied to compiled open-source and new field observations from Archean greenstone terranes in the Abitibi and western Wabigoon sub-provinces of the Superior Province near Timmins and Dryden,Ontario,respectively.Resulting feature maps represent conceptually significant components in magmatic,volcanogenic,and orogenic mineral systems.A comparison of ranked feature importance from random forests to conceptual mineral system models show that the feature maps adequately represent system components,with a few exceptions attributed to biased training data or limited constraint data.The study also highlights the shared importance of several highly ranked features for the three mineral systems,indicating that spatially related mineral systems exploit the same features when available.Comparing feature importance when classifying orogenic Au mineralization in Timmins and Dryden provides insights into the possible cause of contrasting endowment being related to fluid source.The study demonstrates that integrative studies leveraging multidisciplinary data and methodology have the potential to advance geological understanding,maximize data utility,and generate robust exploration targets.展开更多
基金supported by the Geological Society of Australia(Honours Endowment Fund)the Australian Institute of Geoscientists(Honours Bursary)by ARC LP140100267
文摘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.
基金supported by the Exploration Incentive Scheme,administered by the Geological Survey of Western Australia as part of the Royalties for Regions programme of the Western Australian state government
文摘The cratonisation of Western Australia during the Proterozoic overlapped with several key events in the evolution of Earth. These include global oxidation events and glaciations, as well as the assembly,accretionary growth, and breakup of the supercontinents Columbia and Rodinia, culminating in the assembly of Gondwana. Globally, Proterozoic mineral systems evolved in response to the coupled evolution of the atmosphere, hydrosphere, biosphere and lithosphere. Consequently, mineral deposits form preferentially in certain times, but they also require a favourable tectonic setting. For Western Australia a distinct plate-margin mineralisation trend is associated with Columbia, whereas an intraplate mineralisation trend is associated with Rodinia and Gondwana, each with associated deposit types. We compare the current Proterozoic record of ore deposits in Western Australia to the estimated likelihood of oredeposit formation. Overall likelihood is estimated with a simple matrix-based approach that considers two components: The "global secular likelihood" and the "tectonic setting likelihood". This comparative study shows that at least for the studied ore-deposit types, deposits within Western Australia developed at times, and in tectonic settings compatible with global databases. Nevertheless, several deposit types are either absent or poorly-represented relative to the overall likelihood models. Insufficient exploration may partly explain this, but a genuine lack of deposits is also suggested for some deposit types. This may relate either to systemic inadequacies that inhibited ore-deposit formation, or to poor preservation. The systematic understanding on the record of Western Australia helps to understand mineralisation processes within Western Australia and its past connections in Columbia, Rodinia and Gondwana and aids to identify regions of high exploration potential.
文摘Geologically representative feature engineering is a crucial component in geoscientific applications of machine learning.Many commonly applied feature engineering techniques used to produce input variables for machine learning apply geological knowledge to generic data science techniques,which can lead to ambiguity,geological oversimplification,and/or compounding subjective bias.Workflows that utilize minimally processed input variables attempt to overcome these issues,but often lead to convoluted and uninterpretable results.To address these challenges,new and enhanced feature engineering methods were developed by combining geological knowledge,understanding of data limitations,and a variety of data science techniques.These include non-Euclidean fluid pre-deformation path distance,rheological and chemical contrast,geologically constrained interpolation of characteristic host rock geochemistry,interpolation of mobile element gain/loss,assemblages,magnetic intensity,structural complexity,host rock physical properties.These methods were applied to compiled open-source and new field observations from Archean greenstone terranes in the Abitibi and western Wabigoon sub-provinces of the Superior Province near Timmins and Dryden,Ontario,respectively.Resulting feature maps represent conceptually significant components in magmatic,volcanogenic,and orogenic mineral systems.A comparison of ranked feature importance from random forests to conceptual mineral system models show that the feature maps adequately represent system components,with a few exceptions attributed to biased training data or limited constraint data.The study also highlights the shared importance of several highly ranked features for the three mineral systems,indicating that spatially related mineral systems exploit the same features when available.Comparing feature importance when classifying orogenic Au mineralization in Timmins and Dryden provides insights into the possible cause of contrasting endowment being related to fluid source.The study demonstrates that integrative studies leveraging multidisciplinary data and methodology have the potential to advance geological understanding,maximize data utility,and generate robust exploration targets.
