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您的检索式：作者名="Christopher M.Yeomans"
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| 1 | A machine learning approach to tungsten prospectivity modelling using knowledge-driven feature extraction and model confidenc显示文摘Novel mineral prospectivity modelling presented here applies knowledge-driven feature extraction to a datadriven machine learning approach for tungsten mineralisation.The method emphasises the importance of appropriate model evaluation and develops a new Confidence Metric to generate spatially refined and robust exploration targets.The data-driven Random ForestTM algorithm is employed to model tungsten mineralisation in SW England using a range of geological,geochemical and geophysical evidence layers which include a depth to granite evidence layer.Two models are presented,one using standardised input variables and a second that implements fuzzy set theory as part of an augmented feature extraction step.The use of fuzzy data transformations mean feature extraction can incorporate some user-knowledge about the mineralisation into the model.The typically subjective approach is guided using the Receiver Operating Characteristics(ROC)curve tool where transformed data are compared to known training samples.The modelling is conducted using 34 known true positive samples with 10 sets of randomly generated true negative samples to test the random effect on the model.The two models have similar accuracy but show different spatial distributions when identifying highly prospective targets.Areal analysis shows that the fuzzy-transformed model is a better discriminator and highlights three areas of high prospectivity that were not previously known.The Confidence Metric,derived from model variance,is employed to further evaluate the models.The new metric is useful for refining exploration targets and highlighting the most robust areas for follow-up investigation.The fuzzy-transformed model is shown to contain larger areas of high model confidence compared to the model using standardised variables.Finally,legacy mining data,from drilling reports and mine descriptions,is used to further validate the fuzzy-transformed model and gauge the depth of potential deposits.Descriptions of mineralisation corroborate that the targets generated in these models could be undercover at depths of less than 300 m.In summary,the modelling workflow presented herein provides a novel integration of knowledge-driven feature extraction with data-driven machine learning modelling,while the newly derived Confidence Metric generates reliable mineral exploration targets. | Christopher M.Yeomans Robin K.Shail Stephen Grebby Vesa Nykanen Maarit Middleton Paul A.J.Lusty | 2020 | Geoscience Frontiers2020，11，6： | 2 |
| 2 | A machine learning approach for regional geochemical data:Platinum-group element geochemistry vs geodynamic settings of the North Atlantic Igneous Province显示文摘Whilst traditional approaches to geochemistry provide valuable insights into magmatic processes such as melting and element fractionation,by considering entire regional data sets on an objective basis using machine learning algorithms(MLAs),we can highlight new facets within the broader data structure and significantly enhance previous geochemical interpretations.The platinum-group element(PGE)budget of lavas in the North Atlantic Igneous Province(NAIP)has been shown to vary systematically according to age,geographic location and geodynamic environment.Given the large multi-element geochemical data set available for the region,MLAs were employed to explore the magmatic controls on these shifting concentrations.The key advantage of using machine learning in analysis is its ability to cluster samples across multi-dimensional(i.e.,multi-element)space.The NAIP data set is manipulated using Principal Component Analysis(PCA)and t-Distributed Stochastic Neighbour Embedding(t-SNE)techniques to increase separability in the data alongside clustering using the k-means MLA.The new multi-element classification is compared to the original geographic classification to assess the performance of both approaches.The workflow provides a means for creating an objective high-dimensional investigation on a geochemical data set and particularly enhances the identification of metallogenic anomalies across the region.The techniques used highlight three distinct multi-element end-members which successfully capture the variability of the majority of elements included as input variables.These end-members are seen to fluctuate in prominence throughout the NAIP,which we propose reflects the changing geodynamic environment and melting source.Crucially,the variability of Pt and Pd are not reflected in MLA-based clustering trends,suggesting that they vary independently through controls not readily demonstrated by the NAIP major or trace element data structure(i.e.,other proxies for magmatic differentiation).This data science approach thus highlights that PGE(here signalled by Pt/Pd ratio)may be used to identify otherwise localised or cryptic geochemical inputs from the subcontinental lithospheric mantle(SCLM)during the ascent of plume-derived magma,and thereby impact upon the resulting metallogenic basket. | Jordan J.Lindsay Hannah S.R.Hughes Christopher M.Yeomans Jens C.Ø.Andersen Iain McDonald | 2021 | Geoscience Frontiers2021，12，3： | 0 |
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