About

GemPy Probability

Overview

GemPy Probability is a package that extends the functionality of the GemPy package to include uncertainty quantification and stochastic geological modeling. It is based on the Pyro probabilistic programming framework and allows for the integration of probabilistic models into the geological modeling workflow.

(better option), or read the docs.

Contents:

• About
• Installation
  • Installing GemPy
  • Manual installation
  • Dependencies
  • Docker
  • Windows installation guide (March 2020)
  • MacOS installation guide (May 2020)

Probabilistic modeling for Structural Geology

• Intro
  • Problem introduction
  • License
• 1 - First example of inference
  • Simple example using Pyro: Moving the bell
  • Type of probabilistic models
  • Priors as joint probability
• 2 - Probabilistic Geomodel
  • Probability density transformation
  • License
• 3 - Probabilistic Geophysics (WIP)
  • Joint probability
  • License
• Utilities

API Reference

• Code
  • GemPy API
  • Plot
• Data Classes
  • Modeling Classes
  • Helper Classes
  • Enumerators

Stochastic geological modeling

One of the most advanced features that sets GemPy also apart from available commercial packages is the full integration of stochastic geological modeling methods. GemPy was designed from the ground up to support stochastic geological modeling for uncertainty analysis (e.g. Monte Carlo simulations, Bayesian inference). This was achieved by writing GemPy’s core architecture using the numerical computation library aesara to couple it with the probabilistic programming framework PyMC3. This enables the use of advanced sampling methods (e.g. Hamiltonian Monte Carlo) and is of particular relevance when considering uncertainties in the model input data and making use of additional secondary information in a Bayesian inference framework.

We can, for example, include uncertainties with respect to the z-position of layer boundaries in the model space. Simple Monte Carlo simulation via PyMC will then result in different model realizations.

Pytorch allows the automated computation of gradients, opening the door to the use of advanced gradient-based sampling methods coupling GemPy and Pyro (see Pyro’s documentation for advanced stochastic modeling. Also, the use of aesara allows making use of GPUs through cuda (see the aesara documentation for more information.

For a more detailed elaboration of the theory behind GemPy, we refer to the open access scientific publication: “GemPy 1.0: open-source stochastic geological modeling and inversion” by de la Varga et al. (2019).

References

• de la Varga, M., Schaaf, A., and Wellmann, F.: GemPy 1.0: open-source stochastic geological modeling and inversion, Geosci. Model Dev., 12, 1–32, https://doi.org/10.5194/gmd-12-1-2019, 2019.

Indices and tables

• Index

• Search Page