simpeg.data.SyntheticData#

class simpeg.data.SyntheticData(survey, dobs=None, dclean=None, relative_error=None, noise_floor=None)[source]#

Bases: Data

Synthetic data class.

The SyntheticData class is a simpeg.data.Data class that allows the user to keep track of both clean and noisy data.

Parameters:

surveysimpeg.survey.BaseSurvey: A SimPEG survey object. For each geophysical method, the survey object defines the survey geometry; i.e. sources, receivers, data type.
dobsnumpy.ndarray: Observed data.
dclean(nD) numpy.ndarray: Noiseless data.
relative_errorfloat or np.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. \(C_{err} \times |d|\).
noise_floorfloat or np.ndarray: Assign floor/absolute uncertainties to the data. For each datum, we assume standard deviation of Gaussian noise is equal to noise_floor.

Attributes

`dclean`	Vector of the clean synthetic data.
`dobs`	Vector of the observed data.
`index_dictionary`	Dictionary for indexing data by sources and receiver.
`nD`	The number of observed data
`noise_floor`	Noise floor of the data.
`relative_error`	Relative error of the data.
`shape`	The shape of the array containing the observed data
`standard_deviation`	Return data uncertainties; i.e. the estimates of the standard deviations of the noise.
`survey`	The survey for this data.

Methods

`fromvec`(v)	Convert data to vector and assign to observed data
`tovec`()	Convert observed data to a vector