PyHydroGeophysX.uncertainty package#

Submodules#

PyHydroGeophysX.uncertainty.posterior module#

Posterior covariance and uncertainty propagation helpers.

PyHydroGeophysX.uncertainty.posterior.linearized_posterior(J, Cd, Cm_prior)[source]#

Compute linearized Gaussian posterior covariance:

Cm_post = (J^T Cd^-1 J + Cm_prior^-1)^-1

PyHydroGeophysX.uncertainty.posterior.model_resolution_spread(R)[source]#

Return diagonal resolution spread metrics from a resolution matrix.

PyHydroGeophysX.uncertainty.posterior.propagate_petro_uncertainty(rho, rho_cov, petro_func: Callable[[ndarray], ndarray], n_samples: int = 500, seed: int | None = None) Dict[str, ndarray][source]#

Propagate resistivity uncertainty through a petrophysical transform.

Uses Monte Carlo sampling by default.

Module contents#

Posterior and uncertainty quantification helpers.

PyHydroGeophysX.uncertainty.linearized_posterior(J, Cd, Cm_prior)[source]#

Compute linearized Gaussian posterior covariance:

Cm_post = (J^T Cd^-1 J + Cm_prior^-1)^-1

PyHydroGeophysX.uncertainty.model_resolution_spread(R)[source]#

Return diagonal resolution spread metrics from a resolution matrix.

PyHydroGeophysX.uncertainty.propagate_petro_uncertainty(rho, rho_cov, petro_func: Callable[[ndarray], ndarray], n_samples: int = 500, seed: int | None = None) Dict[str, ndarray][source]#

Propagate resistivity uncertainty through a petrophysical transform.

Uses Monte Carlo sampling by default.