kasper d0292ce28d 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.
2025-11-20 12:35:20 +01:00
2025-11-18 16:18:12 +01:00
2025-11-18 16:18:12 +01:00

resistivityVES

Binder

Binder environment using pyGIMLI 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:

jupyter notebook VES.ipynb
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Description
Binder environment using pyGIMLI (https://www.pygimli.org/) to do an VES inversion.
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