Welcome to SIPPI’s documentation!

Contents:

• About SIPPI
  • Implemented methods and algorithms
    • Getting started
    • Referencing
  • Acknowledgement
• Install SIPPI
  • Install SIPPI manually from github
    • Manual compilation
      • Compiling VISIM
      • Compiling SNESIM
      • Compiling MPS
    • SGeMS (optional)
• Setting up SIPPI
  • prior: The a priori model
    • Types of a priori models
      • Sequential Gibbs sampling / Conditional Re-sampling
      • Uniform distribution
      • 1D Generalized Gaussian
      • FFTMA - 3D Gaussian model
      • FFTMA - 3D Gaussian model with variable covariance model properties
      • VISIM
      • CHOLESKY - 3D Gaussian model
      • PluriGaussian - ND truncated Gaussian
      • Voronoi
      • MPS
      • SNESIM
      • SNESIM_STD
  • data: Data and data uncertainties/noise
    • Gaussian measurement noise
      • Uncorrelated Gaussian measurement noise
      • Correlated Gaussian measurement noise
    • Gaussian modeling error
  • forward: The forward model:
    • Validating prior, data, and forward
      • sippi_forward_linear.m
      • sippi_forward_traveltime.m
• Sampling the posterior
  • The rejection sampler: : sippi_rejection.m
  • The extended Metropolis sampler: sippi_metropolis.m
    • Controlling the step length
    • Controlling how to perturb the prior (when multiple priors exist)
    • The independent extended Metropolis sampler
    • Annealing schedule
    • Parallel tempering
  • Linear Least Squares inversion: sippi_least_squares.m
• Examples
  • Examples of A priori models
    • Multiple 1D Gaussian prior model
    • ## Multivariate Gaussian prior with unknown covariance model properties.
    • Simulating the cover of Joy Division’s Unknown Pleasers
  • Probilistic covariance/semivariogram indeference
    • Specification of covariance model parameters
    • Inferring a 2D covariance model from the Jura data set - Example of point support
      • Load the Jura data
      • Setting up SIPPI for covariance parameter inference
  • Polynomial line fitting
  • The challenge
  • Solving the problem using SIPPI
    • The data
    • The prior
    • The forward problem
    • Evaluate the prior, data, and forward
  • Sampling the a posterior distribution
    • Using the Metropolis sampler
    • Using the rejection sampler
  • Cross hole tomography
    • Reference data set from Arrenæs
    • Cross hole travel time delay computation: The forward problem
      • Ray type forward model (high frequency approximation)
      • Fat Ray type forward model (finite frequency approximation)
      • Born type forward model (finite frequency approximation)
      • The eikonal equation (high frequency approximation)
    • Cross hole traveltime inversion of GPR data obtained from Arrenæs: Inversion of cross hole GPR data from Arrenaes
      • Setting up the data structure
      • Setting up the prior model
      • Setting up the forward structure
      • Testing the setup
      • Sampling the a posterior distribution using the extended Metropolis algorithm
    • AM13 Gaussian with bimodal velocity distribution
    • AM13 Gaussian, Linear least squares tomography
  • Cross hole GPR tomopgraphy using Neural Networks
• Bibliography

Indices and tables

• Index
• Module Index
• Search Page