gstools.field.upscaling¶
GStools subpackage providing upscaling routines for the spatial random field.
The following functions are provided
var_coarse_graining (model[, point_volumes]) |
Coarse Graning procedure to upscale the variance for uniform flow. |
var_no_scaling (model, *args, **kwargs) |
Dummy function to bypass scaling. |
-
gstools.field.upscaling.
var_coarse_graining
(model, point_volumes=0.0)[source]¶ Coarse Graning procedure to upscale the variance for uniform flow.
Parameters: - model (
CovModel
) – Covariance Model used for the field. - point_volumes (
float
ornumpy.ndarray
) – Volumes of the elements at the given points. Default:0
Returns: scaled_var – The upscaled variance
Return type: Notes
This procedure was presented in [Attinger03]. It applies the upscaling procedure ‘Coarse Graining’ to the Groundwater flow equation under uniform flow on a lognormal distributed conductivity field following a gaussian covariance function. A filter over a cube with a given edge-length is applied and an upscaled conductivity field is obtained. The upscaled field is again following a gaussian covariance function with scale dependent variance and length-scale:
Therby will be calculated from the given
point_volumes
by assuming a cube with the given volume.The upscaled length scale will be ignored by this routine.
References
[Attinger03] Attinger, S. 2003, ‘’Generalized coarse graining procedures for flow in porous media’’, Computational Geosciences, 7(4), 253–273. - model (