Co-simulated Size Number: An Elegant Novel Algorithm for Identification of Multivariate Geochemical Anomalies

Madani, Nasser; Carranza, Emmanuel John M.

doi:10.1007/s11053-019-09547-9

Cited by 26 publications

(3 citation statements)

References 66 publications

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“…(Figure 12), it is intuitively determined that PPMT produces satisfactory outputs in terms of reproduction of variance. However, minor deviations, such as for Fe 2 O 3 , as can be seen from this figure, are referred to the influence of conditioning data [24,50,51]. However, this tiny departure of average of variances for 100 realizations from original variance is not remarkably significant.…”

Section: Validationmentioning

confidence: 81%

Stochastic Modeling of Chemical Compounds in a Limestone Deposit by Unlocking the Complexity in Bivariate Relationships

Battalgazy

Madani

2019

Minerals

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Modeling multivariate variables with complexity in a cross-correlation structure is always applicable to mineral resource evaluation and exploration in multi-element deposits. However, the geostatistical algorithm for such modeling is usually challenging. In this respect, projection pursuit multivariate transform (PPMT), which can successfully handle the complexity of interest in bivariate relationships, may be particularly useful. This work presents an algorithm for combining projection pursuit multivariate transform (PPMT) with a conventional (co)-simulation technique where spatial dependency among variables can be defined by a linear model of co-regionalization (LMC). This algorithm is examined by one real case study in a limestone deposit in the south of Kazakhstan, in which four chemical compounds (CaO, Al2O3, Fe2O3, and SiO2) with complexity in bivariate relationships are analyzed and 100 realizations are produced for each variable. To show the effectiveness of the proposed algorithm, the outputs (realizations) are statistically examined and the results show that this methodology is legitimate for reproduction of original mean, variance, and complex cross-correlation among the variables and can be employed for further processes. Then, the applicability of the concept is demonstrated on a workflow to classify this limestone deposit as measured, indicated, or inferred based on Joint Ore Reserves Committee (JORC) code. The categorization is carried out based on two zone definitions, geological, and mining units.

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Section: Validationmentioning

confidence: 81%

Stochastic Modeling of Chemical Compounds in a Limestone Deposit by Unlocking the Complexity in Bivariate Relationships

Battalgazy

Madani

2019

Minerals

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“…Geostatistical tools and algorithms are widely used for modeling regionalized data and solving spatial interpolation problems for both natural and anthropogenic variables [ 12 , 13 , 14 , 15 , 16 , 17 , 18 ]. In this context, we decided to apply geostatistical methods to model cigarette butts and broken glass on a beach in Antofagasta, since these two items respectively represent the most common and the most dangerous forms of litter.…”

Section: Methodsmentioning

confidence: 99%

Multivariate Geostatistical Modeling and Risk Analysis of Beach Litter: A Case Study of Playa Blanca Beach, Chile

Maleki

Soria

2020

IJERPH

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Beach litter is a worldwide problem that has several negative effects. A first step in preventing an environmental hazard is to determine and model the level of contamination. In this paper, geostatistical simulation is used to model two main forms of beach litter (cigarette butts and sharp items) in one of the most contaminated beaches in Antofagasta, Chile. A hundred realizations of cigarette butts and broken glass are generated to emulate their joint spatial distribution. The simulation results are used to classify the beach into different areas with respect to the risk of injury by broken glass and the level of contamination by cigarette butts. The models obtained can be used by local authorities in beach clean-up programs and by visitors to beaches in choosing the safest and cleanest areas. The results demonstrate the capability of geostatistical simulation algorithms to model different types of beach litter.

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“…Typical examples of fractal and multifractal methods include the number-size model (Mandelbrot, 1983), concentration-area model, perimeter-area model (Cheng et al, 1994), spectrum-area model (Xu and Cheng, 2001), concentration-distance model (Li et al, 2003), multifractal singular value decomposition model (Li and Cheng, 2004), and singularity theory (Cheng, 2007). In recent years, even more fractal models have been proposed, e.g., the co-simulated size-number model (Madani and Carranza, 2020), global simulated size-number model (Madani and Sadeghi, 2019), concentration-distance from centroids model (Sadeghi and Cohen, 2021a), category-based fractal model (Sadeghi and Cohen, 2021b), and concentration-concentration model (Sadeghi et al, 2021). These earlier exploratory studies well demonstrated the quantitative and qualitative identification and characterization of mineralization-related spatial and frequency properties from multisource observational data sets.…”

Section: Geographic Information System-based Mineral Explorationmentioning

confidence: 99%

Geographically weighted regression in mineral exploration: A new application to investigate mineralization

Wang

Zhao

Cheng

2023

Recent Advancement in Geoinformatics and Data Science

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Geographically weighted regression (GWR) is an effective model for the investigation of spatially nonstationary relations among variables in the geographical and social sciences. GWR was introduced to the field of mineral exploration to further understanding of the location, controlling factors, and coupling mechanisms related to the triggering of mineralization—in other words, the where, what, and how. Previous studies reported that Cu and Au in a porphyry system present a paragenetic relation at different stages of mineralization, which can be an informative indicator in mineral exploration. As a successor, the current study further applies the GWR model to characterize the paragenetic relation between the ore-forming elements Cu and Au in the Duolong mineral district of Tibet, China, in a spatial scenario. Unlike the spatially varied ore-forming mechanism quantified by the regression coefficients of GWR, the coefficient of determination (R2) is discussed to verify the existence and to evaluate the strength of the paragenetic relation between Cu and Au, because regression coefficients can only inform the mutual influence between one and the other. Furthermore, the fractal and multifractal-based spectrum–area method is adopted to separate the GWR results into anomaly and background. Areas with GWR results that indicate the existence and intensity of a paragenetic relation are mapped as target areas for mineral exploration. The current quantitative recognition of mineralization represents a meaningful and useful extension to the application and interpretation of the GWR model.

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Co-simulated Size Number: An Elegant Novel Algorithm for Identification of Multivariate Geochemical Anomalies

Cited by 26 publications

References 66 publications

Stochastic Modeling of Chemical Compounds in a Limestone Deposit by Unlocking the Complexity in Bivariate Relationships

Stochastic Modeling of Chemical Compounds in a Limestone Deposit by Unlocking the Complexity in Bivariate Relationships

Multivariate Geostatistical Modeling and Risk Analysis of Beach Litter: A Case Study of Playa Blanca Beach, Chile

Geographically weighted regression in mineral exploration: A new application to investigate mineralization

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