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

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dc.contributor.author Battalgazy, Nurassyl
dc.contributor.author Madani, Nasser
dc.date.accessioned 2020-03-20T10:01:08Z
dc.date.available 2020-03-20T10:01:08Z
dc.date.issued 2019-11-04
dc.identifier.citation Battalgazy, & Madani. (2019). Stochastic Modeling of Chemical Compounds in a Limestone Deposit by Unlocking the Complexity in Bivariate Relationships. Minerals, 9(11), 683. MDPI AG. Retrieved from http://dx.doi.org/10.3390/min9110683 en_US
dc.identifier.uri https://doi.org/10.3390/min9110683
dc.identifier.uri http://nur.nu.edu.kz/handle/123456789/4542
dc.description.abstract 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. en_US
dc.language.iso en en_US
dc.publisher MDPI en_US
dc.rights Attribution-NonCommercial-ShareAlike 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by-nc-sa/3.0/us/ *
dc.subject mineral resource classification en_US
dc.subject JORC code en_US
dc.subject limestone deposit en_US
dc.subject project pursuit multivariate transform en_US
dc.subject (co)-simulation en_US
dc.title Stochastic Modeling of Chemical Compounds in a Limestone Deposit by Unlocking the Complexity in Bivariate Relationships en_US
dc.type Article en_US
workflow.import.source science


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