GIS-based mineral prospectivity mapping using machine learning methods: A case study from Tongling ore district, eastern China

Sun, Tao; Chen, Fei; Zhong, Laifu; Liu, Weiming; Wang, Yun

doi:10.1016/j.oregeorev.2019.04.003

Cited by 160 publications

(67 citation statements)

References 98 publications

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“…Various MLAs are available for prospectivity modelling, however, it is the Random Forest algorithm that has consistently proven to be highly effective in comparison to Support Vector Machines and Artificial Neural Networks Laborte, 2015a, 2015b;Rodriguez-Galiano et al, 2015;Carranza and Laborte, 2016;Sun et al, 2019). For this reason, two Random Forest models are presented for prospectivity modelling, using: (i) standardized input variables with no transformation; (ii) variables transformed using the guided fuzzy set theory approach of Nykänen et al (2015Nykänen et al ( , 2017).…”

Section: Machine Learning For Prospectivity Modellingmentioning

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%

“…MLAs are now commonplace in mineral prospectivity modelling. The Random Forest, Support Vector Machine and Artificial Neural Network algorithms are regularly implemented and it is the Random Forest MLA that is proving most effective in comparison studies (Rodriguez-Galiano et al, 2015;Sun et al, 2019).…”

Section: Introductionmentioning

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: Machine Learning For Prospectivity Modellingmentioning

confidence: 99%

Section: Random Forest Modellingmentioning

confidence: 99%

See 1 more Smart Citation

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

show abstract

“…Geographic Information System (GIS) tools have been used to produce integrative maps through digital overlay methods [8,38,39]. Knowledge-driven approaches integrating multicriteria decision-making depending on expert judgement have also been used to produce predictive maps [40][41][42].…”

Section: Introductionmentioning

confidence: 99%

Application of remote sensing and GIS techniques for exploring potential areas of hydrothermal mineralization in the central Eastern Desert of Egypt

Shi

Al‐Arifi

Abdelkareem

et al. 2020

Journal of Taibah University for Science

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The integration of remotely sensed data allowed the successful characterization of the hydrothermal alteration zones of the Abu-Gaharish area in the Central Eastern Desert of Egypt using image transformation techniques. The integration of several evidential maps highlighted the plausible areas with high concentrations of argillic and phyllic, key hydrothermal minerals that reflect the intensity of hydrothermal effects and the probable sites of ore bodies. The excellent prospective zone identified in the map occupies around 1.39% of the study area. Kaolinite-smectite minerals with correlation scores of around 0.97 were consistent with areas of hydrothermal alteration and mining activities; however, propylitic minerals were confined to basic metavolcanics and serpentinites. The resulting predictive map was assessed using existing mining data with significant consistency; therefore, field verification is essential. The overall results showed that remotely sensed data represent a valuable mapping tool for geological and mineral resources reconnaissance in the arid regions ARTICLE HISTORY

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“…It is accordingly worthwhile to first provide a brief summary of the commonly used data-and knowledgedriven methods for predicting mineral prospectivity that can potentially be used to evaluate petroleum resource potential. In recent decades, several data-driven methods have been developed and successfully applied in MPM such as logistic regression [2], weights of evidence (WofE) [3], fuzzy WofE [4], boost WofE [5], support vector machine [6], artificial neural networks [6], Bayesian network classifiers [7], decision tree analysis [8], random forests [9], isolation forest [10], certainty factor [11], extreme learning machines [12], and maximum entropy [13]. Boolean logic, index overlay [14], wildcat mapping [15], fuzzy logic [16], data envelopment analysis [17], PROMETHEE [1], ELECTRE [18], AHP [19], and TOPSIS [20] are examples of knowledge-driven methods.…”

Section: Introductionmentioning

confidence: 99%

An ABC-optimized fuzzy ELECTRE approach for assessing petroleum potential at the petroleum system level

Hamzeh

Karimipour

2020

Open Geosciences

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An inevitable aspect of modern petroleum exploration is the simultaneous consideration of large, complex, and disparate spatial data sets. In this context, the present article proposes the optimized fuzzy ELECTRE (OFE) approach based on combining the artificial bee colony (ABC) optimization algorithm, fuzzy logic, and an outranking method to assess petroleum potential at the petroleum system level in a spatial framework using experts’ knowledge and the information available in the discovered petroleum accumulations simultaneously. It uses the characteristics of the essential elements of a petroleum system as key criteria. To demonstrate the approach, a case study was conducted on the Red River petroleum system of the Williston Basin. Having completed the assorted preprocessing steps, eight spatial data sets associated with the criteria were integrated using the OFE to produce a map that makes it possible to delineate the areas with the highest petroleum potential and the lowest risk for further exploratory investigations. The success and prediction rate curves were used to measure the performance of the model. Both success and prediction accuracies lie in the range of 80–90%, indicating an excellent model performance. Considering the five-class petroleum potential, the proposed approach outperforms the spatial models used in the previous studies. In addition, comparing the results of the FE and OFE indicated that the optimization of the weights by the ABC algorithm has improved accuracy by approximately 15%, namely, a relatively higher success rate and lower risk in petroleum exploration.

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GIS-based mineral prospectivity mapping using machine learning methods: A case study from Tongling ore district, eastern China

Cited by 160 publications

References 98 publications

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

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

Application of remote sensing and GIS techniques for exploring potential areas of hydrothermal mineralization in the central Eastern Desert of Egypt

An ABC-optimized fuzzy ELECTRE approach for assessing petroleum potential at the petroleum system level

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