Geochemical distribution mapping by combining number-size multifractal model and multiple indicator kriging

Mohammadpour, Mahyadin; Bahroudi, Abbas; Abedi, Maysam; Rahimipour, Gholamreza; Jozanikohan, Golnaz; Khalifani, Farzaneh Mami

doi:10.1016/j.gexplo.2019.01.018

Cited by 16 publications

(4 citation statements)

References 49 publications

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“…With this system of n + 1 equations, the weights λ i are calculated so that the estimation variance is minimum and the estimator is unbiased. In addition, because it contains the variogram function in its formulation, the Kriging system also considers the structural aspect of the phenomenon under study [12] [19]. The coefficients λ i resulting from the Kriging system are the weights in the calculation of the mineral ore grade estimated at point x:…”

Section: From Which It Follows Thatmentioning

confidence: 99%

Review of Matheron’s Kriging Method and its Application at the Estimation of Mineral Deposits

Ballón¹,

Jimenéz-Pacheco²,

Rondón³

et al. 2019

VUCSM

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The Geostatistics provides effective tools for the solution of many problems of engineering in which the location in the space of the variable under study is considered, based on definitions of mathematics that provide the necessary foundation for its application. In particular, the Geostatistics are applied in the spatial estimation of the recoverable reserves of mineral deposits. The geostatistical methods that are used in the estimation of mineral deposits are implemented in industrial software and consider the evaluation of the complex geological structure, but these softwares only display the obtained results with an input data and do not exhibit the concepts thatthey use during the process or the methodology of its application. This happens particularly with the Kriging method, which is based on the assumption of strict stationarity, taking into account changes in the mean and local variations, therefore unreliable. In this study is established to review the Kriging method, its application in the estimation of the recoverable reserves of mining deposits and the relevance of the developed model established particularly in mines ofPeru, which use this method as part of the mining exploration for the evaluation of the feasibility of exploitation.

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Section: From Which It Follows Thatmentioning

confidence: 99%

Review of Matheron’s Kriging Method and its Application at the Estimation of Mineral Deposits

Ballón¹,

Jimenéz-Pacheco²,

Rondón³

et al. 2019

VUCSM

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“…Separation of these communities can affect the results of classifying elements by means of the methods used. Integration of different methods (e.g., machine learning and geostatistical techniques) can reduce the methods' weaknesses and intensify their advantages for an accurate geochemical modeling of ore mineralization [44][45][46][47][48][49][50].…”

Section: Introductionmentioning

confidence: 99%

Geochemical Modeling of Copper Mineralization Using Geostatistical and Machine Learning Algorithms in the Sahlabad Area, Iran

Shirazi,

Hezarkhani,

Shirazy

et al. 2023

Minerals

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Analyzing geochemical data from stream sediment samples is one of the most proactive tools in the geochemical modeling of ore mineralization and mineral exploration. The main purpose of this study is to develop a geochemical model for prospecting copper mineralization anomalies in the Sahlabad area, South Khorasan province, East Iran. In this investigation, 709 stream sediment samples were analyzed using inductively coupled plasma mass spectrometry (ICP-MS), and geostatistical and machine learning techniques. Subsequently, hierarchical analysis (HA), Spearman’s rank correlation coefficient, concentration–area (C–A) fractal analysis, Kriging interpolation, and descriptive statistics studies were performed on the geochemical dataset. Machine learning algorithms, namely K-means clustering, factor analysis (FA), and linear discriminant analysis (LDA) were employed to deliver a comprehensive geochemical model of copper mineralization in the study area. The identification of trace elements and the predictor composition of copper mineralization, the separation of copper geochemical communities, and the investigation of the geochemical behavior of copper vs. its trace elements were targeted and accomplished. As a result, the elements Ag, Mo, Pb, Zn, and Sn were distinguished as trace elements and predictors of copper geochemical modeling in the study area. Additionally, geochemical anomalies of copper mineralization were identified based on trace elements. Conclusively, the nonlinear behavior of the copper element versus its trace elements was modeled. This study demonstrates that the integration and synchronous use of geostatistical and machine learning methods can specifically deliver a comprehensive geochemical modeling of ore mineralization for prospecting mineral anomalies in metallogenic provinces around the globe.

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“…In particular, some tools allow avoiding the preprocessing of data, such as kriging [12], [13]. Moreover, sediments' ability to absorb organic and inorganic pollution becomes this kind of study is an important tool for monitoring water quality and heavy metals mobility [14].…”

Section: Introductionmentioning

confidence: 99%

Characterization of River Sediments in Loja-Ecuador

Lescano

Mayorga

Martínez-Gómez

2021

Int. J. Adv. Sci. Eng. Inf. Technol.

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This research's main objective is to delineate areas with a high concentration of "pollutant" elements that imply a risk for the ecosystem and inhabitants' health in the Cordillera Real, south of Ecuador. To this end, a survey was carried out applying statistical analysis of the data. Specifically, the method of ordinary Kriging and Lepeltier is used to sort the data in populations according to their concentration. Previously, the information was compared with national (TULAS) and international regulations (Environmental Canada). These metals' spatial distribution is shown in concentration maps for each element (Hg, Pb, Zn, As, and Cu), where the potential villages exposed to these anomalies are displayed. In this sense, the samples' chemical digestion was conducted to quantify the atomic emission technique's before-mentioned pollutants' concentration. It was also correlated with geology, mineral occurrences, and metallogeny evidence to conclude that Pb and Zn anomalies are related to San Lucas granodiorite's intrusion, whereas Cu and Hg with local mineralization of sulfides, and as may be with domestic and industrial discharges. Finally, even though the anomalous concentrations of metallic elements were found to be characteristic of the lithology, cautions should be taken to safeguard the health of people and agriculture because there is evidence of elements such as As and Hg bioaccumulation in species that are part of the food chain.

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Geochemical distribution mapping by combining number-size multifractal model and multiple indicator kriging

Cited by 16 publications

References 49 publications

Review of Matheron’s Kriging Method and its Application at the Estimation of Mineral Deposits

Review of Matheron’s Kriging Method and its Application at the Estimation of Mineral Deposits

Geochemical Modeling of Copper Mineralization Using Geostatistical and Machine Learning Algorithms in the Sahlabad Area, Iran

Characterization of River Sediments in Loja-Ecuador

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