The coefficient of variation; a guide to the sampling of ore deposits

Koch, George S.; Link, Richard F.

doi:10.2113/gsecongeo.66.2.293

Cited by 23 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The earth's crust meets both these criteria for channelized flow (permeability inhomogeneity and pink noise, scale invariant, spatial ln permeability distribution) as the following references in various areas attest: oil/gas (USEIA 2011), geothermal [30], mineral deposition [31][32][33][34][35][36] and trace elements [37][38][39][40]. The spatially heterogeneous, length-scale dependence of permeability is documented from lab-scale cm to reservoir-scale km [8,[41][42][43][44][45].…”

Section: Summary and Discussionmentioning

confidence: 99%

Observational and Critical State Physics Descriptions of Long-Range Flow Structures

Malin

Leary²,

Cathles

et al. 2020

Geosciences

View full text Add to dashboard Cite

Using Fracture Seismic methods to map fluid-conducting fracture zones makes it important to understand fracture connectivity over distances greater 10–20 m in the Earth’s upper crust. The principles required for this understanding are developed here from the observations that (1) the spatial variations in crustal porosity are commonly associated with spatial variations in the magnitude of the natural logarithm of crustal permeability, and (2) many parameters, including permeability have a scale-invariant power law distribution in the crust. The first observation means that crustal permeability has a lognormal distribution that can be described as κ ≈ κ 0 exp ( α ( φ − φ 0 ) ) , where α is the ratio of the standard deviation of ln permeability from its mean to the standard deviation of porosity from its mean. The scale invariance of permeability indicates that αϕο = 3 to 4 and that the natural log of permeability has a 1/k pink noise spatial distribution. Combined, these conclusions mean that channelized flow in the upper crust is expected as the distance traversed by flow increases. Locating the most permeable channels using Seismic Fracture methods, while filling in the less permeable parts of the modeled volume with the correct pink noise spatial distribution of permeability, will produce much more realistic models of subsurface flow.

show abstract

Section: Summary and Discussionmentioning

confidence: 99%

Observational and Critical State Physics Descriptions of Long-Range Flow Structures

Malin

Leary²,

Cathles

et al. 2020

Geosciences

View full text Add to dashboard Cite

show abstract

“…Given the lack of previous geochemical data for soils in the study area, for each individual element, the median value ± 2 Median Absolute Deviations (MAD) was utilized (Reimann et al, 2005 ) to calculate the geochemical baseline interval (which is representative of the actual content of an element in the superficial environment at a given area excluding outliers) (Albanese et al, 2018 ); subsequently, the upper threshold of the interval was used as the UBL. The CV was determined using the ratio of the standard deviation to the average concentration of each element (Koch & Link, 1971 ) to define its own extent of variability within the entire geochemical dataset.…”

Section: Methodsmentioning

confidence: 99%

A new approach to assess the degree of contamination and determine sources and risks related to PTEs in an urban environment: the case study of Santiago (Chile)

Aruta

Albanese

Daniele

et al. 2022

Environ Geochem Health

View full text Add to dashboard Cite

In 2017, a geochemical survey was carried out across the Commune of Santiago, a local administrative unit located at the center of the namesake capital city of Chile, and the concentration of a number of major and trace elements (53 in total) was determined on 121 topsoil samples. Multifractal IDW (MIDW) interpolation method was applied to raw data to generate geochemical baseline maps of 15 potential toxic elements (PTEs); the concentration–area (C-A) plot was applied to MIDW grids to highlight the fractal distribution of geochemical data. Data of PTEs were elaborated to statistically determine local geochemical baselines and to assess the spatial variation of the degree of soil contamination by means of a new method taking into account both the severity of contamination and its complexity. Afterwards, to discriminate the sources of PTEs in soils, a robust Principal Component Analysis (PCA) was applied to data expressed in isometric log-ratio (ilr) coordinates. Based on PCA results, a Sequential Binary Partition (SBP) was also defined and balances were determined to generate contrasts among those elements considered as proxies of specific contamination sources (Urban traffic, productive settlements, etc.). A risk assessment was finally completed to potentially relate contamination sources to their potential effect on public health in the long term. A probabilistic approach, based on Monte Carlo method, was deemed more appropriate to include uncertainty due to spatial variation of geochemical data across the study area. Results showed how the integrated use of multivariate statistics and compositional data analysis gave the authors the chance to both discriminate between main contamination processes characterizing the soil of Santiago and to observe the existence of secondary phenomena that are normally difficult to constrain. Furthermore, it was demonstrated how a probabilistic approach in risk assessment could offer a more reliable view of the complexity of the process considering uncertainty as an integral part of the results.

show abstract

“…The smaller the Cv, the more uniform the mixture is. However, it presents different categorizations according to the different applications that have been used [49][50][51][52][53][54]. Now, Koch & Link 1971 [54] mentioned three important characteristics where the use of Cv is relevant:…”

Section: Phase Iv: Complementary Analysismentioning

confidence: 99%

Integration of Lineal Geostatistical Analysis and Computational Intelligence to Evaluate the Batch Grinding Kinetics

2022

View full text Add to dashboard Cite

The kinetic characterization of the grinding process has always faced a special challenge due to the constant fluctuations of its parameters. The weight percentage of each size (WPES) should be mentioned. There are particular considerations for WPESs, because their tendencies are not monotonic. The objective of this work is to provide a methodology and model that will allow us to better understand the kinetics of grinding through the analysis of the Response Surface (RS), using geostatistical (data reconstruction) and computational intelligence (meta-model) techniques. Six experimental cases were studied and trends were evaluated/adjusted with multiple parameters, including an identity plot adjusted to 0.75–0.90, a standardized error histogram with a mean of −0.01 to −0.05 and a standard deviation of 0.63–1.2, a standardized error based on an estimated value of −0.09 to −0.02, a meta-model adjusted to between 92 and 99%, and finally, using the coefficient of variation, which classifies the information (stable/unstable). In conclusion, it was feasible to obtain the results of the WPES from RS, and it was possible to visualize the areas of greatest fluctuation, trend changes, error adjustments, and data scarcity without the need for specific experimental techniques, a coefficient analysis of the fracturing or the use of differential equation systems.

show abstract

The coefficient of variation; a guide to the sampling of ore deposits

Cited by 23 publications

References 0 publications

Observational and Critical State Physics Descriptions of Long-Range Flow Structures

Observational and Critical State Physics Descriptions of Long-Range Flow Structures

A new approach to assess the degree of contamination and determine sources and risks related to PTEs in an urban environment: the case study of Santiago (Chile)

Integration of Lineal Geostatistical Analysis and Computational Intelligence to Evaluate the Batch Grinding Kinetics

Contact Info

Product

Resources

About