Data- and knowledge-driven mineral prospectivity maps for Canada's North

Harris, Jeff; Grunsky, Eric; Behnia, Pouran; Corrigan, D

doi:10.1016/j.oregeorev.2015.01.004

Cited by 98 publications

(25 citation statements)

References 42 publications

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“…However, it is the class probabilities, the likelihood that a pixel is classified correctly, that are of value when considering prospectivity (Harris et al, 2015). It is good practice to evaluate the accuracy of the prospectivity models, most commonly through the ROC curve tool (Agterberg and Bonham-Carter, 2005;Fawcett, 2006;Robinson and Larkins, 2007;Nykänen, 2008).…”

Section: The Receiver Operating Characteristics (Roc) Curve Toolmentioning

confidence: 99%

“…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%

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

Yeomans¹,

Shail²,

Grebby³

et al. 2019

Preprint

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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.

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Section: The Receiver Operating Characteristics (Roc) Curve Toolmentioning

confidence: 99%

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

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“…In a previous study (Harris et al 2015), the same geochemical dataset was used; however, the geochemical data were corrected for closure (Aitchison 1986) and censored values. They found that when RF was applied to the corrected or raw geochemical data, there was little difference in the output classification results.…”

Section: Lake Sediment Geochemical Datamentioning

confidence: 99%

“…The main point of ensemble classifiers, such as RF, is that the process learns from not just one prediction (decision tree) but from many predictions that are then combined (Doan and Foody 2007; Harris et al 2015). This is extremely beneficial as this process helps to reduce the variance as the results are less dependent on peculiarities of a single training dataset.…”

Section: Random Forests Classification Algorithmmentioning

confidence: 99%

“…While this is not a new method of producing mineral potential maps (Rodriguez-Galiano et al 2014;Carranza and Laborte 2015;Harris et al 2015), the strength of RF, in this case, comes from its ability to also create a probability (confidence) map for the classification of each pixel. It is this probability map, as opposed to the classified map, that is used as a measure of prospectivity (Harris et al 2015). The KD technique we employ involves a combination of basic index overlay and fuzzy-weighted index overlay approaches.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of the Data-Driven Random Forests Model and a Knowledge-Driven Method for Mineral Prospectivity Mapping: A Case Study for Gold Deposits Around the Huritz Group and Nueltin Suite, Nunavut, Canada

2015

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This paper outlines the process taken to create two separate gold prospectivity maps. The first was created using a combination of several knowledge-driven (KD) techniques. The second was created using a relatively new classification method called random forests (RF). The purpose of this study was to examine the results of the RF technique and to compare the results to that of the KD model. The datasets used for the creation of evidence maps for the gold prospectivity mapping include a comprehensive lake sediment geochemical dataset, interpreted geological structures (form lines), mapped and interpreted faults, lithology, topographic features (lakes), and known Au occurrences. The RF method performed well in that the gold prospectivity map created was a better predictor of the known Au occurrences than the KD gold prospectivity map. This was further validated by a fivefold repetition using a subset of the input training areas. Several advantages to the use of RF include (1) the ability to take both continuous and/or categorical data as variable inputs, (2) an internal, unbiased estimation of the mapping error (out-of-bag error) removing the need for a cross-validation of the final outputs to determine accuracy, and (3) the estimation of importance of each input variable. Efficiency of prediction curves illustrates that the RF method performs better than the KD method. The success rate is significantly higher for the RF method than for the KD method.

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