simpeg.meta.DaskSumMetaSimulation.make_synthetic_data

DaskSumMetaSimulation.make_synthetic_data(m, relative_error=0.05, noise_floor=0.0, f=None, add_noise=False, random_seed=None, **kwargs)

Make synthetic data for the model and Gaussian noise provided.

This method generates and returns a simpeg.data.SyntheticData object for the model and standard deviation of Gaussian noise provided.

Parameters:

m(n_param, ) numpy.ndarray

The model parameters.

relative_errorfloat, numpy.ndarray

Assign relative uncertainties to the data using relative error; sometimes referred to as percent uncertainties. For each datum, we assume the standard deviation of Gaussian noise is the relative error times the absolute value of the datum; i.e. \(C_\text{err} \times |d|\).

noise_floorfloat, numpy.ndarray

Assign floor/absolute uncertainties to the data. For each datum, we assume standard deviation of Gaussian noise is equal to noise_floor.

fsimpeg.fields.Fields, optional

If provided, fields will not need to be recomputed when solving the forward problem to obtain noiseless data.

add_noisebool

Whether to add gaussian noise to the synthetic data or not.

random_seedNone or RandomSeed, optional

Random seed used for random sampling. It can either be an int or a predefined Numpy random number generator (see numpy.random.default_rng).

Returns:

simpeg.data.SyntheticData

A SimPEG synthetic data object, which organizes both clean and noisy data.