Incorporation of geological constraints and semivariogram scaling law into geostatistical modeling of metal contents in hydrothermal deposits for improved accuracy

Koike, Katsuaki; Kiriyama, Takuya; Lü, Lixing; Kubo, Taiki; Heriawan, Mohamad Nur; Yamada, Ryoichi

doi:10.1016/j.gexplo.2021.106901

Cited by 10 publications

(2 citation statements)

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“…Semivariance is a function of the distance between the observed and unknown data points. It measures the average degree of dissimilarity between unsampled sites and nearby data values (Koike et al, 2022). Semivariance is proportional to spatial distribution of data points, the lower semivariance associated to lower distance and vice versa.…”

Section: Semivariancementioning

confidence: 99%

“…The selected semivariogram model influences the interpolation of the unknown or missing data. The more the shape of the semivariogram curve is approaching the realistic investigations, the more accurately interpolation will be achieved at virtual or uninvestigated stations (Koike et al, 2022). In other words, the steeper the curve nearby the origin, the higher influence of closed neighbor points on the interpolated surface (Raju, 2016).…”

Section: Semivariogram Modelsmentioning

confidence: 99%

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Spatial distribution prediction for the ground water quality in Mosul City (Iraq) using variogram equations

Ibrahim

Al-Tamir

2023

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The GIS-aided spatial interpolation was applied on collected groundwater data to predict selected parameters (i.e., pH, electrical conductivity, and temperature) for the selected water wells distributed over Mosul City in Iraq. A descriptive statistical analysis was conducted on collected samples to explore the statistical indices. The skewness test was also employed to test the distribution of data sets around their mean values. The natural logarithms function achieved least skewness values and thus was applied to transfer data sets in order to adjust normality of the data sets distribution. Among all applied semivariogram models, the J-Bessel semivariogram model was optimal in terms of root mean square error (RMSE) values. The average standard errors were 0.2217, 740.5, and 1.209 for pH, EC, and temperature, respectively.

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Section: Semivariancementioning

confidence: 99%

Section: Semivariogram Modelsmentioning

confidence: 99%