.. Mineye-Terranigma documentation master file

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Mineye-Terranigma Documentation
==============================

Overview
========

``Mineye-Terranigma`` is a research project for **probabilistic geological modeling**
and **geophysical inversion**. The project combines state-of-the-art implicit geological
modeling with Bayesian inference techniques.

**Core Capabilities:**

* **3D Geological Modeling**: Build implicit 3D models from structural data using `GemPy <https://www.gempy.org>`_
* **Geophysical Forward Modeling**: Compute gravity and magnetic responses from geological models
* **Probabilistic Inference**: Quantify uncertainty using `Pyro <https://pyro.ai>`_ and PyTorch
* **Satellite Image Segmentation**: Bayesian classification of remote sensing data
* **Joint Inversion**: Integrate multiple data sources for improved geological interpretations

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Getting Started
===============

.. toctree::
   :maxdepth: 1

   installation

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.. _basic-examples:

Basic Examples
==============

Foundational workflows for 3D geological modeling and geophysical forward modeling.
These examples introduce core concepts using real data from the **Tharsis mining district** in Spain.

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.. _probabilistic-examples:

Probabilistic Modeling
======================

Advanced examples demonstrating uncertainty quantification, error propagation,
and Bayesian inversion techniques for geological and geophysical applications.


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.. _segmentation-examples:

Hyperspectral Segmentation
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Workflows for lithological segmentation of EnMap hyperspectral data using Bayesian inference.
These examples cover feature extraction, preprocessing, and model comparison.

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Key Features
============

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   * - **Bayesian Inference with Pyro**
     - **Geological Modeling with GemPy**
   * - The project uses PyTorch and Pyro for probabilistic programming, enabling:

       * **Variational Inference (VI)**: Fast approximate posterior estimation
       * **Hamiltonian Monte Carlo (HMC/NUTS)**: Accurate sampling for complex posteriors
       * **GPU Acceleration**: Scalable inference for large-scale inversions
     - Building on the GemPy framework, the project supports:

       * **Implicit Surface Modeling**: Continuous 3D geological surfaces from sparse data
       * **Structural Complexity**: Faults, unconformities, and erosive contacts
       * **Forward Modeling**: Gravity and magnetic field computations
       * **Uncertainty Quantification**: Probabilistic geological interpretations


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API Reference
=============

Detailed documentation of the Mineye-Terranigma Python API.

.. toctree::
   :maxdepth: 2

   api_reference

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Indices and Tables
==================

* :ref:`genindex`
* :ref:`search`