Random Field Generation

The main feature of GSTools is the spatial random field generator SRF, which can generate random fields following a given covariance model. The generator provides a lot of nice features, which will be explained in the following

GSTools generates spatial random fields with a given covariance model or semi-variogram. This is done by using the so-called randomization method. The spatial random field is represented by a stochastic Fourier integral and its discretised modes are evaluated at random frequencies.

In case you want to generate spatial random fields with periodic boundaries, you can use the so-called Fourier method. See the corresponding examples for how to do that. The spatial random field is represented by a stochastic Fourier integral and its discretised modes are evaluated at equidistant frequencies.

GSTools supports arbitrary and non-isotropic covariance models.

Examples

A Very Simple Example

A Very Simple Example

Creating an Ensemble of Fields

Creating an Ensemble of Fields

Creating Fancier Fields

Creating Fancier Fields

Using an Unstructured Grid

Using an Unstructured Grid

Merging two Fields

Merging two Fields

Generating Fields on Meshes

Generating Fields on Meshes

Using PyVista meshes

Using PyVista meshes

Higher Dimensions

Higher Dimensions

Generating a Simple Periodic Random Field

Generating a Simple Periodic Random Field

Generating a Transformed Periodic Random Field

Generating a Transformed Periodic Random Field