Neuro-fuzzy modelling in mining geochemistry: Identification of geochemical anomalies

Ziaii, Mansour; Pouyan, Ali A.; Ziaei, Mahdi

doi:10.1016/j.gexplo.2008.03.004

Cited by 49 publications

(15 citation statements)

References 17 publications

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“…Input variables can be subdivided into the following: supra-ore, upper-ore, ore, lower-ore, and sub-ore [24]. Ziaii et al (2012) and Ziaii et al (2009) showed that these groups provide the necessary information to separate BM from ZDM in porphyry-Cu mineralization [2,3].…”

Section: Methodology 41 Zonality Methodsmentioning

confidence: 99%

Multispectral Remote Sensing Satellite Data for Mineral and Hydrocarbon Exploration: Big Data Processing and Deep Fusion Learning Techniques

2023

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Section: Methodology 41 Zonality Methodsmentioning

confidence: 99%

Multispectral Remote Sensing Satellite Data for Mineral and Hydrocarbon Exploration: Big Data Processing and Deep Fusion Learning Techniques

2023

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“…Asimismo, las estadísticas multivariables como el coeficiente de rango correlacional, análisis factorial (Tripathi, 1979;Reimann et al, 2002;Liu et al, 2016), el análisis de componentes principales (Zuo, 2011;Cheng et al, 2011;Zuo et al, 2013;Chen et al, 2019) se ha convertido en una poderosa herramienta de prospección basado en las asociaciones geoquímicas de los depósitos minerales. Estas técnicas de estadísticas univariables y multivariables pueden usarse de forma integrada a través de inferencias difusas (fuzzy modelling) (Ziaii et al, 2009;Yousefi et al, 2014;Moradi et al, 2015) para generar mapas de prospectividad a nivel regional y local. Con el desarrollo de la inteligencia artificial, aprendizaje automático (machine learning) y aprendizaje profundo (deep learning) surgen nuevas formas de integrar una gran cantidad de datos geoquímicos y usarlos en la cartografía geoquímica para localizar nuevos targets de exploración (Kirkwood et al, 2016;Zuo, 2017;Zuo & Xiong, 2018;Zuo et al, 2019).…”

Section: Introductionunclassified

Innovaciones estadísticas y redes neuronales artificiales aplicadas a la prospección geoquímica en los cuadrángulos de Chaparra y Atico, Caravelí – Arequipa

Cruz

2021

Rev. Inst. investig. Fac. minas metal cienc. geogr

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La prospección geoquímica siempre ha sido y seguirá siendo una de las herramientas más efectivas para la búsqueda de recursos minerales, que se integra con la cartografía geológica base y otras disciplinas como la teledetección y la geofísica. Esta investigación pretende impulsar una nueva metodología de prospección geoquímica combinando el índice de enriquecimiento relativo local (LREI) de elementos indicadores de mineralización (Cu, Au, Ag, Pb, Zn y Mo), coeficientes de rango correlacional (RCC) y el análisis de componentes principales (PCA), a través de superposiciones difusas (fuzzy modelling) y el uso de aprendizaje profundo (deep learning) no supervisado, para generar mapas de prospectividad geoquímica en los cuadrángulos de Chaparra (32o) y Atico (33o) en la provincia de Caravelí, Arequipa. El mapa de prospectividad generado mediante superposición difusa y el mapa autoorganizado (SOM) fueron validados ploteando la ubicación de los depósitos y ocurrencias minerales conocidos, los cuales en la mayoría de los casos se plotean dentro de áreas con alta o moderada prospectividad. Por lo que se concluye que las innovaciones estadísticas univariables, multivariables e innovaciones tecnológicas aplicadas a la cartografía geoquímica son herramientas efectivas para la prospección geoquímica.

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“…Meshkani et al (2011) used hierarchical and k-means clustering for identifying distribution of lead and zinc in the Sanandaj-Sirjan metallogenic zone in Iran. Ziaii et al (2009) introduced the neuro-fuzzy method for separating anomalies and showed that this method is more efficient than using multivariate statistics. Ellefsen and Smith (2016) evaluated a clustering method called the Bayesian finite mixture modeling procedure by applying it to geochemical data collected in the State of Colorado, United States of America.…”

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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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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Neuro-fuzzy modelling in mining geochemistry: Identification of geochemical anomalies

Cited by 49 publications

References 17 publications

Multispectral Remote Sensing Satellite Data for Mineral and Hydrocarbon Exploration: Big Data Processing and Deep Fusion Learning Techniques

Multispectral Remote Sensing Satellite Data for Mineral and Hydrocarbon Exploration: Big Data Processing and Deep Fusion Learning Techniques

Innovaciones estadísticas y redes neuronales artificiales aplicadas a la prospección geoquímica en los cuadrángulos de Chaparra y Atico, Caravelí – Arequipa

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

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