Updates model uncertainty visualization in Jupyter notebook

Refines presentation of model uncertainty by adding error bars to the resistivity and thickness values in the output plot.

Integrates a new table displaying layer resistivity, uncertainty, and thickness with corresponding uncertainties to enhance clarity for users.

Updates environment requirements to include `pandas` for improved data manipulation capabilities.

README.md

@@ -1,5 +1,63 @@
-[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/git/https%3A%2F%2Fgit.geophysik.ruhr-uni-bochum.de%2Fkasper%2FresistivityVES.git/HEAD?urlpath=%2Fdoc%2Ftree%2FVES.ipynb)
-
 # resistivityVES
 
-Binder environment using pyGIMLI (https://www.pygimli.org/) to do an VES inversion.
+[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/git/https%3A%2F%2Fgit.geophysik.ruhr-uni-bochum.de%2Fkasper%2FresistivityVES.git/HEAD?urlpath=%2Fdoc%2Ftree%2FVES.ipynb)
+
+Binder environment using [pyGIMLI](https://www.pygimli.org/) to do a VES (Vertical Electrical Sounding) inversion.
+
+## Overview
+
+This repository contains a Jupyter notebook (`VES.ipynb`) that demonstrates how to perform 1D DC resistivity inversion using pyGIMLI's built-in VES forward operator. The notebook uses real field data from Bausenberg to invert for a layered earth model.
+
+## Notebook Contents
+
+The `VES.ipynb` notebook includes the following workflow:
+
+### 1. Setup and Imports
+
+- Imports necessary libraries: `numpy`, `matplotlib`, `pygimli`, and the `VESManager` from `pygimli.physics`
+
+### 2. Field Data from Bausenberg
+
+- Uses real VES measurements with AB/2 distances ranging from 1.0 to 100.0 meters
+- Apparent resistivity values (`rhoa`) showing variations from ~64 to ~672 Ωm
+- Error estimates set at 2% for most measurements, increasing to 5% for deeper soundings
+- MN/2 spacing fixed at 0.5 meters
+
+### 3. Inversion Setup
+
+- Configures a 3-layer earth model (`nlay=3`)
+- Uses regularization parameter `lam=1000` with a reduction factor of 0.8
+- Inverts the apparent resistivity data to determine layer thicknesses and resistivities
+
+### 4. Visualization
+
+The notebook provides comprehensive visualization including:
+
+- **Model plot**: Displays the inverted resistivity model as a function of depth (up to 50m)
+- **Data fit plot**: Compares measured apparent resistivity data with the model response
+- Both plots are displayed side-by-side for easy comparison
+
+### 5. Uncertainty Analysis
+
+- Computes model covariance matrix to assess parameter uncertainties
+- Displays correlation matrix showing interdependencies between layer parameters
+- Generates error bars for both resistivities and layer thicknesses
+- Visualizes uncertainties at layer midpoints and boundaries
+
+## Requirements
+
+See `environment.yml` for the complete list of dependencies. Main requirements:
+
+- pyGIMLI>=1.5.0, which requires at least:
+  - numpy
+  - matplotlib
+  - suitesparse=5.10.1
+- jupyterlab
+
+## Usage
+
+Click the Binder badge above to launch an interactive session, or run locally with:
+
+```bash
+jupyter notebook VES.ipynb
+```

environment.yml

@@ -8,3 +8,4 @@ dependencies:
   - pygimli>=1.5.0
   - jupyterlab
   - suitesparse=5.10.1
+  - pandas