BaseElectricalPDESimulation.make_synthetic_data(m, relative_error=0.05, noise_floor=0.0, f=None, add_noise=False, random_seed=None, **kwargs)[source]#

Make synthetic data for the model and Gaussian noise provided.

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

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.


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

random_seedint, optional

Random seed to pass to numpy.random.default_rng.


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