simpeg.utils.model_builder.create_random_model

simpeg.utils.model_builder.create_random_model(shape, seed=1000, anisotropy=None, its=100, bounds=None)

Create random model by convolving a kernel with a uniformly distributed random model.

Parameters:

shapeint or tuple of int

Shape of the model. Can define a vector of size (n_cells) or define the dimensions of a tensor

seedNone or RandomSeed, optional

Random seed for random uniform model that is convolved with the kernel. It can either be an int, a predefined Numpy random number generator, or any valid input to numpy.random.default_rng.

anisotropynumpy.ndarray

this is the (3, n) blurring kernel that is used.

itsint

Number of smoothing iterations after convolutions

boundslist of float

Lower and upper bound for the model values

Returns:

numpy.ndarray

Physical property model

Examples

>>> import matplotlib.pyplot as plt
>>> from simpeg.utils.model_builder import create_random_model
>>> m = create_random_model((50,50), bounds=[-4,0])
>>> plt.colorbar(plt.imshow(m))
>>> plt.title('A very cool, yet completely random model.')
>>> plt.show()

(Source code, png, pdf)

Galleries and Tutorials using simpeg.utils.model_builder.create_random_model

Maps: Mesh2Mesh

Maps: Mesh2Mesh