Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Lokālā universālā kriginga metode× | Kopkrigings: Daudzdimensiju ģeostatistiskā interpolācija× | |
|---|---|---|
| Nozare | Telpiskā analīze | Telpiskā analīze |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1969/1997 | 1965-1978 |
| Autors≠ | Matheron, G. (trend/drift kriging); local neighborhood approach standard in geostatistical practice | Matheron, G.; extended by Journel & Huijbregts |
| Tips≠ | Spatial interpolation model | Geostatistical interpolation |
| Pirmavots≠ | Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation. Oxford University Press. ISBN: 9780195115383 | Journel, A. G., & Huijbregts, C. J. (1978). Mining Geostatistics. Academic Press, London. ISBN: 978-0123910561 |
| Citi nosaukumi | local UK, local kriging with trend, local KED, local kriging with external drift | cokriging, co-regionalization kriging, multivariate kriging, CK |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | Local Universal Kriging is a geostatistical interpolation method that combines a spatially varying deterministic trend with a stochastic residual, estimated using only nearby observations within a defined search neighborhood. It generalizes local ordinary kriging by explicitly modeling and removing a polynomial or covariate-driven drift before interpolating the residual surface. | Co-kriging is a geostatistical interpolation technique that predicts the spatial distribution of a primary variable by leveraging its spatial cross-correlation with one or more secondary (co-) variables. It extends ordinary kriging to multivariate settings, yielding more accurate predictions when the secondary variable is more densely sampled or spatially correlated with the primary variable of interest. |
| ScholarGateDatu kopa ↗ |
|
|