The use of decision tree induction and artificial neural networks for recognizing the geochemical distribution patterns of LREE in the Choghart deposit, Central Iran

Zaremotlagh, S.; Hezarkhani, Ardeshir

doi:10.1016/j.jafrearsci.2016.08.018

Cited by 20 publications

(5 citation statements)

References 57 publications

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“…There are two main types of MPM: data-driven models and knowledge-driven models. If a relatively large number of known mineral occurrences and their relationships to explanatory datasets are well known within a study area, data-driven modeling techniques such as weights of evidence (Nyka ¨nen et al, 2008), Monte Carlo simulations (Lysytsyn, 2015), Naı ¨ve Bayes (Zaremotlagh & Hezarkhani, 2016), logistic regression (Xiong & Zuo, 2018), or neural networks (Wang et al, 2011;Zaremotlagh & Hezarkhani, 2017) may be appropriate. Data-driven techniques allow for the quantification of uncertainty due to statistical randomness.…”

Section: Geologic Resource Assessment Methodologiesmentioning

confidence: 99%

A Geo-Data Science Method for Assessing Unconventional Rare-Earth Element Resources in Sedimentary Systems

et al. 2023

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Rare-earth elements (REEs) supply raw materials that constitute many of our modern critical infrastructure, defense, technology, and electrification needs. Despite REE accumulations occurring in conventional bedrock and ion-adsorption deposits sourced from weathering of igneous rocks, unconventional host materials such as coal and related sedimentary strata have been identified as promising sources of REEs to meet growing demand. To maximize the potential of unconventional resources such as REE-coal systems, new approaches are needed overcome challenges from mineral systems with no known deposits and areas with sparse geochemical data. This article presents a systematic knowledge-data resource assessment method for predicting and identifying REE resource potential and occurrence in these unconventional systems. The method utilizes a geologic and geospatial knowledge-data approach informed and guided by REE accumulation mechanisms to systematically assess and identify areas of higher enrichment. An assessment of the Powder River Basin is presented as a test case to demonstrate the method workflow and results. The key output is a potential enrichment score map reported with varying confidence levels based on the amount of supporting evidence. Results from the test case indicate several locations with promising potential for different types of coal-REE deposits, demonstrating the viability of the method for exploration and assessment of unconventional REE resources. The method is flexible by design and, with sufficient applicable knowledge and data, can be adapted for assessing critical mineral systems in other sedimentary systems as well.

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Section: Geologic Resource Assessment Methodologiesmentioning

confidence: 99%

A Geo-Data Science Method for Assessing Unconventional Rare-Earth Element Resources in Sedimentary Systems

et al. 2023

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“…ANN is a modelling method that is inspired by biological neural systems and simulates the pattern recognition ability of the human brain [59,60]. An artificial neuron is the functional unit that processes the received information.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Data-Driven Predictive Modelling of Mineral Prospectivity Using Machine Learning and Deep Learning Methods: A Case Study from Southern Jiangxi Province, China

Sun

Kai-xing

et al. 2020

Minerals

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Predictive modelling of mineral prospectivity, a critical, but challenging procedure for delineation of undiscovered prospective targets in mineral exploration, has been spurred by recent advancements of spatial modelling techniques and machine learning algorithms. In this study, a set of machine learning methods, including random forest (RF), support vector machine (SVM), artificial neural network (ANN), and a deep learning convolutional neural network (CNN), were employed to conduct a data-driven W prospectivity modelling of the southern Jiangxi Province, China. A total of 118 known W occurrences derived from long-term exploration of this brownfield area and eight evidential layers of multi-source geoscience information related to W mineralization constituted the input datasets. This provided a data-rich foundation for training machine learning models. The optimal configuration of model parameters was trained by a grid search procedure and validated by 10-fold cross-validation. The resulting predictive models were comprehensively assessed by a confusion matrix, receiver operating characteristic curve, and success-rate curve. The modelling results indicate that the CNN model achieves the best classification performance with an accuracy of 92.38%, followed by the RF model (87.62%). In contrast, the RF model outperforms the rest of ML models in overall predictive performance and predictive efficiency. This is characterized by the highest value of area under the curve and the steepest slope of success-rate curve. The RF model was chosen as the optimal model for mineral prospectivity in this region as it is the best predictor. The prospective zones delineated by the prospectivity map occupy 9% of the study area and capture 66.95% of the known mineral occurrences. The geological interpretation of the model reveals that previously neglected Mn anomalies are significant indicators. This implies that enrichment of ore-forming material in the host rocks may play an important role in the formation process of wolframite and can represent an innovative exploration criterion for further exploration in this area.

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“…Geochemical anomaly recognition is a key task in mineral exploration [1,2] for the alleviation of the current shortages of mineral resources [3]. A large number of geochemical anomaly recognition methods have been developed in the past few decades [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…A large number of geochemical anomaly recognition methods have been developed in the past few decades [4][5][6]. The recognition of geochemical anomalies is to discover the prospection information contained in geochemical data by identifying the anomalies deviating from the normal geochemical samples (i.e., geochemical background) [3]. Full exploitation of geochemical information can improve the performance of anomaly recognition.…”

Section: Introductionmentioning

confidence: 99%

“…A variety of ANNs, such as deep belief network (DBN) and convolutional neural network (CNN) have been successfully applied in various fields, such as speech recognition [18], image recognition [19], and information retrieval [20]. In the field of geochemical exploration, DL models have also been applied for geochemical anomaly recognition [3,21,22]. For example, DBN has been used to extract the characteristics of the relationships between the elements related to the Fe ore, and to reconstruct the backgrounds to separate anomalies from the background [23].…”

Section: Introductionmentioning

confidence: 99%

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A Multi-Convolutional Autoencoder Approach to Multivariate Geochemical Anomaly Recognition

Chen

Guan

Feng³

et al. 2019

Minerals

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The spatial structural patterns of geochemical backgrounds are often ignored in geochemical anomaly recognition, leading to the ineffective recognition of valuable anomalies in geochemical prospecting. In this contribution, a multi-convolutional autoencoder (MCAE) approach is proposed to deal with this issue, which includes three unique steps: (1) a whitening process is used to minimize the correlations among geochemical elements, avoiding the diluting of effective background information embedded in redundant data; (2) the Global Moran’s I index is used to determine the recognition domain of the background spatial structure for each element, and then the domain is used for convolution window size setting in MCAE; and (3) a multi-convolutional autoencoder framework is designed to learn the spatial structural pattern and reconstruct the geochemical background of each element. Finally, the anomaly score at each sampling location is calculated as the difference between the whitened geochemical features and the reconstructed features. This method was applied to the southwestern Fujian Province metalorganic belt in China, using the concentrations of Cu, Mn, Pb, Zn, and Fe2O3 measured from stream sediment samples. The results showed that the recognition domain determination greatly improved the quality of anomaly recognition, and MCAE outperformed several existing methods in all aspects. In particular, the anomalies from MCAE were the most consistent with the known Fe deposits in the area, achieving an area under the curve (AUC) of 0.89 and a forecast area of 17%.

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The use of decision tree induction and artificial neural networks for recognizing the geochemical distribution patterns of LREE in the Choghart deposit, Central Iran

Cited by 20 publications

References 57 publications

A Geo-Data Science Method for Assessing Unconventional Rare-Earth Element Resources in Sedimentary Systems

A Geo-Data Science Method for Assessing Unconventional Rare-Earth Element Resources in Sedimentary Systems

Data-Driven Predictive Modelling of Mineral Prospectivity Using Machine Learning and Deep Learning Methods: A Case Study from Southern Jiangxi Province, China

A Multi-Convolutional Autoencoder Approach to Multivariate Geochemical Anomaly Recognition

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