Instructors: Devin C. Cowan¹ and Santiago Soler¹

Authors of the material: Devin C. Cowan¹, Santiago Soler¹ and Lindsey Heagy¹

Contributors: Mariana G.² and Rowan Cockett³

¹ Geophysical Inversion Facility. Earth Ocean and Atmospheric Sciences. University of British Columbia.
² Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), Baja California
³ Curvenote

In this workshop we'll showcase how we can use the tools available in SimPEG, a Python library for Simulations and Parameter Estimations in Geophysics, to simulate ERT (a.k.a. DC resistivity) data from a synthetic model and then to run a deterministic inversion to recover the original model. You'll learn how to set up a DC survey, define a mesh, build a synthetic model, create a simulation that implements all the physics, and run a forward model to generate synthetic data. Lastly you'll learn how to set up an inversion to recover the resistivity of the subsurface. By the end of the tutorial you'll be able to use SimPEG to invert your own data.

We were invited by Agrogeo24 to give a workshop about SimPEG, to show the agrogeophysical community how we can use SimPEG to run forward simulations of DC resistivity data (ERT), and how we can invert that type of data.

During this workshop we'll go through two Jupyter notebooks that showcase examples on how we can run forward and inverse problems for ERT data:

• 01-forward-dc-resisitivity-2d.ipynb
• 02-inversion-dc-resistivity-2d.ipynb

During this workshop we'll use Juptyer notebooks to run the forward and inverse modelling of DC resistivity data.

To be able to follow the workshop and run these notebooks, you'll need to have access to a Python environment. This could be done through online services like Google Colab, or through a local Python installation like Anaconda or Miniforge.

By default we'll use Google Colab. But we'll consider installing Python locally in case something goes wrong with the former.

Here will provide instructions to:

• Configure Google Colab
• or Install Python locally

To be able to run the Jupyter notebooks for this tutorial in Google Colab, we'll need to follow these steps:

1. Login to our Google Colab account.
2. Create a new notebook.
3. Install some Python libraries that we'll need to use, such as SimPEG.

If you don't have a Google account, create one and log in. If you do, you just need to log in.

1. Access to Google Colab by going to: https://colab.research.google.com
2. Find the top menu and choose File > New notebook. A new tab should open with a blank notebook in it.

To be able to follow this workshop we need to install some Python libraries that aren't preinstalled in the default Google Colab environment.

1. Click on the first cell of the notebook (if it's not empty, then create a new Code cell and move it to the first position with the arrows icons that appear on its top-right).
2. Type the following line in the selected cell:

   !pip install simpeg==0.20.0 discretize==0.10.0 pymatsolver==0.2.0
   

   Note the ! sign at the beginning of the line, don't remove it.
3. Run that cell by clicking the Play button on its left or by pressing Shift+Enter in your keyboard. pip should install all the packaged listed in that line. If installation goes smoothly, you should see a line that reads Successfully installed ... and lists all the new packages that had been installed.

To be able to run the Jupyter notebooks for this tutorial in our own machines, we'll have to follow these steps:

1. Install a Python distribution (like Anaconda or miniforge).
2. Create a conda environment with all the Python packages needed (for example, SimPEG).
3. Activate this conda environment and run JupyterLab to start coding.

We recommend installing a Python distribution like miniforge or Anaconda.

Both of them will install Python and a package manager that allows us to install new Python libraries (like SimPEG for example), and also create environments.

Anaconda uses the conda package manager, while Miniforge uses the new mamba, which works faster than conda.

If you have either of both installed, you can skip this step. Otherwise, please follow their installation instructions:

• Install miniforge: https://github.com/conda-forge/miniforge#install
• Install Anaconda: https://docs.anaconda.com/anaconda/install

1. Download the environment.yml file from (right-click and select "Save page as" or similar).
2. Make sure that the file is called environment.yml. Windows sometimes adds a .txt to the end, which you should remove.
3. Open a terminal (Anaconda Prompt or Miniforge Prompt if you are running Windows). The following steps should be done in the terminal.
4. Navigate to the folder that has the downloaded environment file (if you don't know how to do this, take a moment to read the Software Carpentry lesson on the Unix shell).
5. Create the conda environment by running mamba env create --file environment.yml (this will download and install all of the packages used in the tutorial). If you installed Anaconda, then replace mamba for conda in the previous line.

Now we can activate the newly created simpeg-agrogeo24 environment.

1. Open a terminal (Anaconda Prompt or Miniforge Prompt if you are running Windows).
2. Activate the simpeg-agrogeo24 environment by running mamba activate simpeg-agrogeo24. If you installed Anaconda, then replace mamba for conda in the previous line.
3. With the simpeg-agrogeo24 environment activated, we can start JupyterLab by running jupyterlab in the terminal. A new tab in our web browser should open showing JupyterLab's interface.

This work is licensed under a Creative Commons Attribution 4.0 International License.