hydrobox.geostat.ext_drift_kriging

hydrobox.geostat.ext_drift_kriging(variogram, grid_resolution, ext_drift, exact=True, cond_err='nugget', pseudo_inv=True, pseudo_inv_type='pinv', return_type='plot', **kwargs)

Use a scikit-gstat Variogram to estimate spatial properties of a sample. Uses this variogram to interpolate using kriging. The Kriging is done with the ExtDrift class. Refer to the docs of ExtDrift to learn about the parameters.

For external drift kriging you need to specify the external drift term of the field. Then, the regression between drift and sample will be taken into account for kriging. This is useful for fields, that are actually correlated to other fields, which are (more) available. If that is not the case, refer to ordinary_kriging for kriging without drift.

Note

Right now, there are only very limited possibilities to specify an interpolation grid. Only setting the resolution of the result like: 50x50 is possible by passing the integer 50. More fine-grained control will be added with a future release.

Parameters

  • variogram (skgstat.Variogram) – Variogram used for kriging

  • grid_resolution (int) – Resoultion of the interpolation grid. The resolution will be used in all input data dimensions, which can lead to non-quadratic grid cells.

  • ext_drift (np.ndarray) – External drift values at the observation points

  • exact (bool) – If True (default), the input data will be matched exactly. Refer to ExtDrift for more info.

  • cond_err (str, float, list) – Measurement error, or variogram nugget. Refer to ExtDrift for more info.

  • pseudo_inv (bool) – If True, the Kriging is more robust, but also slower. Refer to ExtDrift for more info.

  • pseudo_inv_type (str) – Type of matrix inversion used if pseudo_inv is True. Refer to ExtDrift for more info.

  • return_type (str) – Specify how the result should be retuned. Can be the kriging class itself ('object'), the interpolated grid ('grid') or a plot of the grid ('plot').

Return type

Union[List[ndarray], Figure]

Returns

  • results (numpy.ndarray, numpy.ndarray) – Interpolation grid and kriging error grid

  • fig (plotly.graph_objects.Figure, matplotlib.Figure) – Figure of the result plot.

hydrobox.geostat.universal_kriging Hydrobox Examples