Application of Self-Organizing Map for Exploration of REEs’ Deposition

Sarparandeh, Mohammadali; Hezarkhani, Ardeshir

doi:10.4236/ojg.2016.67045

Cited by 8 publications

(5 citation statements)

References 29 publications

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“…These methods have been applied in diverse aspects of science and engineering, somewhat in geochemistry and never for exploration of REEs. The papers of Sarparandeh and Hezarkhani (2016) and Zaremotlagh and Hezarkhani (2016) are the only efforts which have been made in this area. However, there is no study that applies and compares several types of algorithms.…”

Section: Methodsmentioning

confidence: 99%

“…In 2012, Abedi et al the used the SOM and fuzzy k-means techniques to provide a deposit exploration map for the Now Chun copper deposit in Iran. Sarparandeh and Hezarkhani (2016) examined the application of SOMs in evaluation of the geochemical distribution of REEs in the Choghart Fe-REE deposit in Bafq district and showed its good performance. Generally, in cases where there are too many parameters and samples, pattern recognition is a suitable approach for data processing.…”

Section: Introductionmentioning

confidence: 99%

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Application of unsupervised pattern recognition approaches for exploration of rare earth elements in Se-Chahun iron ore, central Iran

Sarparandeh

Hezarkhani

2017

Geosci. Instrum. Method. Data Syst.

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Abstract. The use of efficient methods for data processing has always been of interest to researchers in the field of earth sciences. Pattern recognition techniques are appropriate methods for high-dimensional data such as geochemical data. Evaluation of the geochemical distribution of rare earth elements (REEs) requires the use of such methods. In particular, the multivariate nature of REE data makes them a good target for numerical analysis. The main subject of this paper is application of unsupervised pattern recognition approaches in evaluating geochemical distribution of REEs in the Kiruna type magnetite-apatite deposit of SeChahun. For this purpose, 42 bulk lithology samples were collected from the Se-Chahun iron ore deposit. In this study, 14 rare earth elements were measured with inductively coupled plasma mass spectrometry (ICP-MS). Pattern recognition makes it possible to evaluate the relations between the samples based on all these 14 features, simultaneously. In addition to providing easy solutions, discovery of the hidden information and relations of data samples is the advantage of these methods. Therefore, four clustering methods (unsupervised pattern recognition) -including a modified basic sequential algorithmic scheme (MBSAS), hierarchical (agglomerative) clustering, k-means clustering and selforganizing map (SOM) -were applied and results were evaluated using the silhouette criterion. Samples were clustered in four types. Finally, the results of this study were validated with geological facts and analysis results from, for example, scanning electron microscopy (SEM), X-ray diffraction (XRD), ICP-MS and optical mineralogy. The results of the k-means clustering and SOM methods have the best matches with reality, with experimental studies of samples and with field surveys. Since only the rare earth elements are used in this division, a good agreement of the results with lithology is considerable. It is concluded that the combination of the proposed methods and geological studies leads to finding some hidden information, and this approach has the best results compared to using only one of them.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of unsupervised pattern recognition approaches for exploration of rare earth elements in Se-Chahun iron ore, central Iran

Sarparandeh

Hezarkhani

2017

Geosci. Instrum. Method. Data Syst.

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“…Some studies on Kiruna-type iron oxide-apatite (IOA) deposits in Bafq mining district showed the potential for REEs (i.e. SeChahun [5,35]; Choghart: [34,37]. In addition, some numerical models were applied to predict the hidden patterns of REEs in Choghart iron ore deposit [34,46].…”

Section: Introductionmentioning

confidence: 99%

“…SeChahun [5,35]; Choghart: [34,37]. In addition, some numerical models were applied to predict the hidden patterns of REEs in Choghart iron ore deposit [34,46]. The REEs are mainly concentrated in specific types of rocks and deposits.…”

Section: Introductionmentioning

confidence: 99%

Anomaly separation of La, Ce and Nd in Se-Chahun iron ore deposit, Bafq district, central Iran

Sarparandeh¹,

Hezarkhani²,

Saljooghi³

2017

IJSEA

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Increase in global prices of rare earth elements (REEs) in recent years has attracted many exploration researchers, especially in Iran. There are some promising areas in central Iran which contain significant amounts of light rare earth elements (LREE). SeChahun metasomatic iron ore deposit is one of them. Concentrations of La, Ce and Nd are considerable in some parts of this deposit. On the other hand, one of the most important steps in geochemical exploration of precious elements is separation of anomaly from background. For this purpose, some methods such as classical statistics and fractal models are common. However, application and simplicity are the two main parameters for choosing a proper method. In this study, classical statistics approach (using the mean and standard deviation), and also probability graph and C-A fractal model (Concentration Area) were applied for anomaly-background separation of La, Ce and Nd. Comparing the results of the methods with mineralogy and chemistry of the samples, showed that probability graph had the best performance in anomaly separation. Therefore, considering the results as well as simplicity of the method, it concluded that probability graph is more applicable and a better approach in comparison to others.

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“…(2) The unassigned data are allocated to their appropriate clusters (appendix) (Kainulainen and Kainulainen, 2002). Sarparandeh and Hezarkhani (2016) implemented this method for delineating lithology and exploring rare elements. Jin (1994) also implemented BSAS for two-dimensional subsidence analysis.…”

Section: Modified Basic Sequential Algorithmic Schemementioning

confidence: 99%

Enhanced velocity based pore pressure prediction using lithofacies clustering: A case study from a reservoir with complex lithology in Dezful Embayment, SW Iran

Arabameri¹,

soleymani²,

Tokhmechi³

2018

Preprint

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Velocity based pore pressure prediction methods are widely accepted as a routine technique in the petroleum industry. Despite recent improvements, still, literature suffer from inconsistencies and uncertainties mostly arise from velocity anomalies due to complex lithostratigraphic setting or presence of various formation fluids. The primary goal of this paper is to improve the accuracy and reliability of the conventional Bowers and Tau methods in a reservoir with complex lithology. Our proposed workflow aims to improve the accuracy of the estimations by clustering the input data based on specific petrophysical characteristics. We show since each major zones at the offset test wells have a distinct compaction trend, empirical constants in Bowers and Tau methods can be calibrated for each cluster rather than the whole stratigraphic column. The clustering task was done by statistical analyses of a suite of well logs and validated with core derived lithologies. To find the best clustering algorithm, we applied and compared five techniques namely, K-means, basic sequential algorithmic scheme, single, and complete linkage hierarchical. We found that the self-organizing map (SOM) method provides the best results by maximizing lithology likelihood within each cluster and improve the overall accuracy of the Bowers and Tau methods. This research also aims to provide a systematic comparison of the mentioned clustering algorithms based on their ability in distinguishing various lithofacies. We also try to minimize the user interference in the process of clustering multiple lithofacies and improve the reproducibility of the results and demonstrate the capability of the proposed method through a case study in a reservoir in the Southwest of Iran. Satisfactory results of this study offer a safe ground for implementation of the proposed method in other sedimentary basins.

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Application of Self-Organizing Map for Exploration of REEs’ Deposition

Cited by 8 publications

References 29 publications

Application of unsupervised pattern recognition approaches for exploration of rare earth elements in Se-Chahun iron ore, central Iran

Application of unsupervised pattern recognition approaches for exploration of rare earth elements in Se-Chahun iron ore, central Iran

Anomaly separation of La, Ce and Nd in Se-Chahun iron ore deposit, Bafq district, central Iran

Enhanced velocity based pore pressure prediction using lithofacies clustering: A case study from a reservoir with complex lithology in Dezful Embayment, SW Iran

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