Influence of the Sampling Density in the Coestimation Error of a Regionalized Locally Stationary Variable
In the present study, the influence of the sampling density on the coestimation error of a regionalized, locally stationary and geo-mining nature variable is analyzed. The case study is two-dimensional (2D) and synthetic-type, and it has been generated using a non-conditional Sequential Gaussian Sim...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-01-01
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| Series: | Minerals |
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| Online Access: | https://www.mdpi.com/2075-163X/10/2/90 |
| _version_ | 1811290690074705920 |
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| author | Heber Hernandez Guerra Elisabete Alberdi Aitor Goti |
| author_facet | Heber Hernandez Guerra Elisabete Alberdi Aitor Goti |
| author_sort | Heber Hernandez Guerra |
| collection | DOAJ |
| description | In the present study, the influence of the sampling density on the coestimation error of a regionalized, locally stationary and geo-mining nature variable is analyzed. The case study is two-dimensional (2D) and synthetic-type, and it has been generated using a non-conditional Sequential Gaussian Simulation (SGS), with subsequent transformation to Gaussian distribution, seeking to emulate the structural behavior of the aforementioned variable. A primary and an auxiliary variable with different spatial and statistical properties are constructed using the same methodology. The collocated ordinary cokriging method has been applied, in which the auxiliary variable is spatially correlated with the primary one and it is known exhaustively. Fifteen sampling densities are extracted from the target population of the primary variable, which are compared with the simulated values after performing coestimation. The obtained results follow a potential function that indicates the mean global error (MGE) based on the sampling density percentage (SDP) (<inline-formula> <math display="inline"> <semantics> <mrow> <mi>M</mi> <mi>G</mi> <mi>E</mi> <mo>=</mo> <mn>1.2366</mn> <mo>·</mo> <mi>S</mi> <mi>D</mi> <msup> <mi>P</mi> <mrow> <mo>−</mo> <mn>0.224</mn> </mrow> </msup> </mrow> </semantics> </math> </inline-formula>). |
| first_indexed | 2024-04-13T04:17:09Z |
| format | Article |
| id | doaj.art-fde65f9f8cc34f1d91bddcfd5a740165 |
| institution | Directory Open Access Journal |
| issn | 2075-163X |
| language | English |
| last_indexed | 2024-04-13T04:17:09Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Minerals |
| spelling | doaj.art-fde65f9f8cc34f1d91bddcfd5a7401652022-12-22T03:02:57ZengMDPI AGMinerals2075-163X2020-01-011029010.3390/min10020090min10020090Influence of the Sampling Density in the Coestimation Error of a Regionalized Locally Stationary VariableHeber Hernandez Guerra0Elisabete Alberdi1Aitor Goti2Department of Mining, University of Aconcagua UAC, La Serena 1700000, ChileDepartment of Applied Mathematics, University of the Basque Country UPV/EHU, 48013 Bilbao, Bizkaia, SpainDeusto Digital Industry Chair, University of Deusto, 48007 Bilbao, Bizkaia, SpainIn the present study, the influence of the sampling density on the coestimation error of a regionalized, locally stationary and geo-mining nature variable is analyzed. The case study is two-dimensional (2D) and synthetic-type, and it has been generated using a non-conditional Sequential Gaussian Simulation (SGS), with subsequent transformation to Gaussian distribution, seeking to emulate the structural behavior of the aforementioned variable. A primary and an auxiliary variable with different spatial and statistical properties are constructed using the same methodology. The collocated ordinary cokriging method has been applied, in which the auxiliary variable is spatially correlated with the primary one and it is known exhaustively. Fifteen sampling densities are extracted from the target population of the primary variable, which are compared with the simulated values after performing coestimation. The obtained results follow a potential function that indicates the mean global error (MGE) based on the sampling density percentage (SDP) (<inline-formula> <math display="inline"> <semantics> <mrow> <mi>M</mi> <mi>G</mi> <mi>E</mi> <mo>=</mo> <mn>1.2366</mn> <mo>·</mo> <mi>S</mi> <mi>D</mi> <msup> <mi>P</mi> <mrow> <mo>−</mo> <mn>0.224</mn> </mrow> </msup> </mrow> </semantics> </math> </inline-formula>).https://www.mdpi.com/2075-163X/10/2/90collocated ordinary cokrigingsampling densityregionalizedlocal stationary variables |
| spellingShingle | Heber Hernandez Guerra Elisabete Alberdi Aitor Goti Influence of the Sampling Density in the Coestimation Error of a Regionalized Locally Stationary Variable Minerals collocated ordinary cokriging sampling density regionalized local stationary variables |
| title | Influence of the Sampling Density in the Coestimation Error of a Regionalized Locally Stationary Variable |
| title_full | Influence of the Sampling Density in the Coestimation Error of a Regionalized Locally Stationary Variable |
| title_fullStr | Influence of the Sampling Density in the Coestimation Error of a Regionalized Locally Stationary Variable |
| title_full_unstemmed | Influence of the Sampling Density in the Coestimation Error of a Regionalized Locally Stationary Variable |
| title_short | Influence of the Sampling Density in the Coestimation Error of a Regionalized Locally Stationary Variable |
| title_sort | influence of the sampling density in the coestimation error of a regionalized locally stationary variable |
| topic | collocated ordinary cokriging sampling density regionalized local stationary variables |
| url | https://www.mdpi.com/2075-163X/10/2/90 |
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