Using Random Forests to distinguish gahnite compositions as an exploration guide to Broken Hill-type Pb–Zn–Ag deposits in the Broken Hill domain, Australia

O'Brien, Joshua J.; Spry, Paul G.; Nettleton, Dan; Xu, Ruo; Teale, Graham S.

doi:10.1016/j.gexplo.2014.11.010

Cited by 38 publications

(25 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While notable exceptions exist, such as identifying zones of hydrothermal alteration and host-rock types (Cracknell et al, 2014) and modeling of mineral prospectivity (e.g., Rodriguez-Galiano et al, 2014;Carranza and Laborte, 2015), many other opportunities remain untested. Additionally, only one previous study (O'Brien et al, 2015) used Random Forests analysis of the trace element contents of individual mineral phases (i.e., gahnite), despite the large amount of multielement geochemistry data generated in recent years by LA-ICP-MS. In this contribution we provide a proof of concept-that is, we show how the Random Forests method can be used to classify ore deposit type both as an exploration tool and as a means of identifying samples most representative of primary marine conditions uncompromised by secondary overprints.…”

Section: Introductionmentioning

confidence: 99%

Distinguishing Ore Deposit Type and Barren Sedimentary Pyrite Using Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry Trace Element Data and Statistical Analysis of Large Data Sets

et al. 2019

View full text Add to dashboard Cite

Faced with ongoing depletion of near-surface ore deposits, geologists are increasingly required to explore for deep deposits or those lying beneath surface cover. The result is increased drilling costs and a need to maximize the value of the drill hole samples collected. Laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) analysis of pyrite is one tool that is showing promise in deep exploration. Since the trace element content of pyrite approximates the composition of the fluid from which it precipitated and the crystallization mechanism, the trace element characteristics can be used to predict the type of deposit with which a pyritic sample is associated. This possibility, however, is complicated by overlapping trace element abundances for many deposit types. The solution lies with simultaneous comparison of multiple trace elements through rigorous statistical analysis. Specifically, we used LA-ICP-MS pyrite trace element data and Random Forests, an ensemble machine learning supervised classifier, to distinguish barren sedimentary pyrite and five ore deposit categories: iron oxide copper-gold (IOCG), orogenic Au, porphyry Cu, sedimentary exhalative (SEDEX), and volcanic-hosted massive sulfide (VHMS) deposits. The preferred classifier utilizes in situ Co, Ni, Cu, Zn, As, Mo, Ag, Sb, Te, Tl, and Pb measurements to train the Random Forests. Testing of the Random Forests classifier using additional data from the same deposits and sedimentary basins (test data set) yielded an overall accuracy of 91.4% (94.9% for IOCG, 78.8% for orogenic Au, 81.1% for porphyry Cu, 93.6% for SEDEX, 97.2% for sedimentary pyrite, 91.8% for VHMS). Similarly, testing of the Random Forests classifier using data from deposits and sedimentary basins that did not have analyses in the training data set yielded an overall accuracy of 88.0% (81.4% for orogenic Au, 95.5% for SEDEX, 90.0% for sedimentary pyrite, 73.9% for VHMS; insufficient data was available to perform a blind test on porphyry Cu and IOCG). The performance of the classifier was further improved by instituting criteria (at least 40% of total votes from the Random Forests needed for a conclusive identification) to remove uncertain or inconclusive classifications, increasing the classifier's accuracy to 94.5% for the test data (94.6% for IOCG, 85.8% for orogenic Au, 87.8% for porphyry Cu, 95.4% for SEDEX, 98.5% for sedimentary pyrite, 94.6% for VHMS) and 93.9% for the blind test data (85.5% for orogenic Au, 96.9% for SEDEX, 96.7% for sedimentary pyrite, 84.6% for VHMS). The Random Forests classification models for pyrite trace element data can be used as a predictive modeling tool in greenfield terrains by providing an accurate indication of ore deposit type. This advance will assist mineral explorers by allowing early implementation of predictive ore deposit models when prospecting for ore deposits. Furthermore, the ability of the classifier to accurately identify pyrite of sedimentary origin will allow researchers interested in paleoenvironmental conditions of ...

show abstract

Section: Introductionmentioning

confidence: 99%

Distinguishing Ore Deposit Type and Barren Sedimentary Pyrite Using Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry Trace Element Data and Statistical Analysis of Large Data Sets

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The method has become increasingly popular in geoscience and has been used in prospectivity modelling for a range of ore deposit types (e.g. O'Brien et al, 2014;Harris et al, 2015;Carranza & Laborte 2015a, 2015bGao et al, 2017;Hariharan et al, 2017;Li et al, 2019;Sun et al, 2019). The approach combines multiple binary-split trees which limits overfitting that can occur through multi-split trees (Hastie et al, 2009).…”

Section: Random Forest Modellingmentioning

confidence: 99%

A machine learning approach to tungsten prospectivity modelling using knowledge-driven feature extraction and model confidence

Yeomans¹,

Shail²,

Grebby³

et al. 2019

Preprint

View full text Add to dashboard Cite

Novel mineral prospectivity modelling presented here applies knowledge-driven feature extraction to a data-driven 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 Forest™ 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.

show abstract

“…Multivariate classification is widely and successfully used in science (e.g., Haaland et al, 1997), and has many applications in geosciences and mineral exploration (Schetselaar et al, 2000;Cracknell et al, 2014;Abbaszadeh et al, 2015;Carranza and Laborte, 2015;O'Brien et al, 2015) including lithological discrimination in VMS environments (e.g., Fresia et al, 2017). Multivariate classification resorts to using several variables (X1, X2,…, Xn-1, Xn) that describe a set of samples, and that will allow to discriminate between classes among these samples.…”

Section: Multivariate Classificationmentioning

confidence: 99%

“…Machine learning is increasingly being used to aid interpretation of geological data (e.g., O'Brien et al, 2015;Rodriguez-Galiano et al, 2015;Sadeghi and Carranza, 2015;Kirkwood et al, 2016). Contrary to traditional geochemical classification diagrams, which are generally limited to two or three variables at a time (e.g., Pearce and Norry, 1979;De La Roche et al, 1980;Wood, 1980;Verma and Agrawal, 2011), machine learning algorithms such as neural networks and support vector machines allow for the simultaneous use of multiple variables.…”

Section: Introductionmentioning

confidence: 99%

Classification of lithostratigraphic and alteration units from drillhole lithogeochemical data using machine learning: A case study from the Lalor volcanogenic massive sulphide deposit, Snow Lake, Manitoba, Canada

Caté

Schetselaar

Mercier-Langevin

et al. 2018

Journal of Geochemical Exploration

View full text Add to dashboard Cite

Classification of rock types using geochemical variables is widely used in geosciences, but most standard classification methods are restricted to the simultaneous use of two or three variables at a time. Machine learning-based methods allow for a multivariate approach to classification problems, potentially increasing classification success rates. Here a series of multivariate machine learning classification algorithms, together with different sets of lithogeochemistry-derived variables, are tested on samples collected at the Lalor Zn-Cu-Au volcanogenic massive sulphide deposit, to discriminate volcanic units and alteration types. Support Vector Machine and Ensemble method algorithms give the best performance on both classification exercises. Untransformed chemical element concentrations with high classification power are the best-performing variables. Classification success rates are equal or better than those obtained using standard classification methods and are satisfactory enough for the use of the resulting predictions for 2D and 3D modelling of geological units. Highlights • Machine learning algorithms are used for multivariate geochemical classification. • Volcanic units and alteration types are discriminated using untransformed chemical element concentrations. • Support Vector Machine and Ensemble methods yield the highest classification success scores.

show abstract

Using Random Forests to distinguish gahnite compositions as an exploration guide to Broken Hill-type Pb–Zn–Ag deposits in the Broken Hill domain, Australia

Cited by 38 publications

References 54 publications

Distinguishing Ore Deposit Type and Barren Sedimentary Pyrite Using Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry Trace Element Data and Statistical Analysis of Large Data Sets

Distinguishing Ore Deposit Type and Barren Sedimentary Pyrite Using Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry Trace Element Data and Statistical Analysis of Large Data Sets

A machine learning approach to tungsten prospectivity modelling using knowledge-driven feature extraction and model confidence

Classification of lithostratigraphic and alteration units from drillhole lithogeochemical data using machine learning: A case study from the Lalor volcanogenic massive sulphide deposit, Snow Lake, Manitoba, Canada

Contact Info

Product

Resources

About