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

Minerals ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. 683 ◽  
Author(s):  
Battalgazy ◽  
Madani
Keyword(s):  
Mineral Resource ◽  
Linear Model ◽  
Stochastic Modeling ◽  
Cross Correlation ◽  
Projection Pursuit ◽  
Chemical Compounds ◽  
Spatial Dependency ◽  
Mean Variance ◽  
Mineral Resource Evaluation

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.

scholarly journals Mean-Variance QTL Mapping Identifies Novel QTL for Circadian Activity and Exploratory Behavior in Mice

2018 ◽  
Author(s):  
Robert W. Corty ◽  
Vivek Kumar ◽  
Lisa M. Tarantino ◽  
Joseph S. Takahashi ◽  
William Valdar
Keyword(s):  
Qtl Mapping ◽  
Linear Model ◽  
Wheel Running ◽  
Interval Mapping ◽  
Residual Variance ◽  
Running Activity ◽  
First Case ◽  
Wheel Running Activity ◽  
Mean Variance

ABSTRACTWe illustrate, through two case studies, that “mean-variance QTL mapping” can discover QTL that traditional interval mapping cannot. Mean-variance QTL mapping is based on the double generalized linear model, which elaborates on the standard linear model by incorporating not only a linear model for the data itself, but also a linear model for the residual variance. Its potential for use in QTL mapping has been described previously, but it remains underutilized, with certain key advantages undemonstrated until now. In the first case study, we use mean-variance QTL mapping to reanalyze a reduced complexity intercross of C57BL/6J and C57BL/6N mice examining circadian behavior and find a mean-controlling QTL for circadian wheel running activity that was not detected by traditional interval mapping. Mean-variance QTL mapping was more powerful than traditional interval mapping at the QTL because it accounted for the fact that mice homozygous for the C57BL/6N allele had less residual variance than the other mice. In the second case study, we reanalyze an intercross between C57BL/6J and C58/J mice examining anxiety-like behaviors, and identify a variance-controlling QTL for rearing behavior. This QTL was not identified in the original analysis because traditional interval mapping does not target variance QTL.

scholarly journals Mineral Resource Depletion Assessment: Alternatives, Problems, Results

2021 ◽  
Vol 13 (2) ◽  
pp. 862
Author(s):  
Tatiana Ponomarenko ◽  
Marina Nevskaya ◽  
Izabela Jonek-Kowalska
Keyword(s):  
Mineral Resource ◽  
Natural Resources ◽  
Resource Depletion ◽  
Mining Company ◽  
Social Wellbeing ◽  
Renewable Natural Resources ◽  
Tin Deposit ◽  
The Impact ◽  
Local Levels

The depletion of non-renewable natural resources (primarily mineral and energy resources) and its assessment is a problem that is analyzed based on the concept of sustainable development. Mineral resource depletion assessment is particularly important for resource-based economies. It provides for assessing the impact of mineral asset disposal that results from the suspension or termination of operations conducted by a mining company due to insurmountable circumstances. The results of such an event will be manifested at the national, regional, and local levels and felt by mining companies, suppliers, workers, the population of the territory, and other stakeholders. The study clarifies the attributes and essence of mineral resource depletion, analyzes the advantages and limitations of the existing tools for assessing mineral resource depletion, identifies depletion factors, describes a methodology for assessing mineral resource depletion, and contains a case study of a tin deposit. The results of the study contribute to the development of the theory on the depletion of non-renewable natural resources. They provide for assessing losses to social wellbeing that can be caused by stopping the use of profitable mineral reserves.

A NON-LINEAR MODEL FOR OPTIMAL ALLOCATION OF IRRIGATION WATER AND LAND UNDER ADEQUATE AND LIMITED WATER SUPPLIES: A CASE STUDY IN SOUTHERN ITALY

2013 ◽  
Vol 62 (2) ◽  
pp. 145-155 ◽  
Author(s):  
Pietro Rubino ◽  
Anna Maria Stellacci ◽  
Roberta M. Rana ◽  
Maurizia Catalano ◽  
Angelo Caliandro
Keyword(s):  
Linear Model ◽  
Irrigation Water ◽  
Optimal Allocation ◽  
Southern Italy ◽  
Water Supplies ◽  
Non Linear
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