uncertainties module

uncertainties.abs_precision_error(x, x_mean, N)[source]
uncertainties.generate_gauss(abs_mean, mean_unc, N)[source]

Generate an array of absolute Gaussian random numbers.

Parameters:

  • abs_mean (float) – The absolute mean of the Gaussian distribution.

  • mean_unc (float) – The standard deviation (uncertainty) of the Gaussian distribution.

  • N (int) – The number of random numbers to generate.

Returns:

An array of absolute Gaussian random numbers.

Return type:

numpy.ndarray

uncertainties.rho_err(constants, geotherm, profile_type='Vp', N=1000, z_slices=100, make_plots=False, save_plots=False, outpath='../UNCERTAINTY_PLOTS')[source]

Calculate crustal density uncertainty by Monte Carlo sampling of thermal and compressibility parameters. Returns drho(z) for mean correction, and standard deviation.

Note that this does not include the uncertainty associated with the velocity-pressure calibration, which should be combined separately if necessary. This function can only handle a single profile at a time. Note that the Geotherm class must be initialised with the desired parameters including the geotherm parameters (including tc) and uncertainties.

Parameters:

  • constants (object) – Instance of the Constants class containing the constants for the calculation.

  • geotherm (object) – Geotherm object for temperature dependence. The rho attribute of the geotherm object will be used to set the bulk density for the density error propagation.

  • profile_type (str, optional) – Type of velocity profile, ‘Vp’ or ‘Vs’. Default is ‘Vp’.

  • N (int, optional) – Number of samples for the Monte Carlo simulation. Default is 1000.

  • z_slices (int, optional) – Number of depth slices for the calculation. Default is 100.

  • make_plots (bool, optional) – If True, generates plots of the results. Default is False.

  • save_plots (bool, optional) – If True, saves the generated plots. Default is False.

  • outpath (str, optional) – Path to save the plots. Default is “../UNCERTAINTY_PLOTS”.

Returns:

  • error_average_frac (float) – The average fractional error calculated from the Monte Carlo simulation.

  • error_average_add (float) – The average additive error calculated from the Monte Carlo simulation.