simpeg.electromagnetics.frequency_domain.simulation.BaseFDEMSimulation.make_synthetic_data#
- BaseFDEMSimulation.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
simpeg.data.SyntheticDataobject for the model and standard deviation of Gaussian noise provided.- Parameters:
- m(
n_param, )numpy.ndarray The model parameters.
- relative_error
float,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_floor
float,numpy.ndarray Assign floor/absolute uncertainties to the data. For each datum, we assume standard deviation of Gaussian noise is equal to noise_floor.
- f
simpeg.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_seed
NoneorRandomSeed,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).
- m(
- Returns:
simpeg.data.SyntheticDataA SimPEG synthetic data object, which organizes both clean and noisy data.