Probabilistic Modeling¶
In the dynamic and often uncertain realm of subsurface exploration, probabilistic modeling emerges as a key tool. This section delves into the application of Bayesian Statistics and probabilistic approaches to address the inherent uncertainties in subsurface data. Here’s what you’ll learn:
Embracing Uncertainty: Gain an understanding of how to quantify and incorporate uncertainty in geological models. We focus on aleatoric uncertainty, which represents the inherent variability in subsurface data.
Bayesian Methods with Pyro: Discover how to apply Bayesian statistical methods using Pyro, a powerful probabilistic programming framework. These tutorials will guide you through the process of encoding uncertainty in your models, offering a robust way to handle complex geological data.
Integrating Structural Uncertainty: Learn to integrate structural uncertainty into your models using GemPy. This approach enhances the realism and reliability of your geological interpretations.
Probabilistic Inversions: Explore how geophysical data can be used as observational constraints in probabilistic inversions. This technique allows for a more comprehensive understanding of subsurface structures by incorporating both geophysical measurements and geological knowledge.
