Here we present a gravity inversion algorithm for modelling bathymetry. This is a non-linear geometric regularized least-squares inversion. Pre-existing bathymetry measurements can be used to constrain the inversion, and a Bayesian approach, via Monte Carlo simulation, is used to estimate uncertainties and sensitivity of the inversion to the various input data and parameters.
This inversion was developed as part of my PhD thesis. Chapter 3 of the thesis tests the inversion on a suite of synthetic and semi-synthetic models. The relevant Jupyter notebooks for this are in notebooks/synthetic_inversion and notebooks/Ross_Sea_inversion.
Chapter 4 from the thesis uses the inversion to model the bathymetry beneath Antarctica's Ross Ice Shelf. The relevant Jupyter notebooks for this are in notebooks/Ross_Ice_Shelf_inversion. This includes notebooks for levelling and reducing the airborne gravity data.
Below are instructions for using this repository.
You can download a copy of all the files for this project by cloning the GitHub repository:
git clone https://github.com/mdtanker/RIS_gravity_inversion
Install the required dependencies with either conda or mamba:
cd RIS_gravity_inversion
make conda_install
Activate the newly created environment:
conda activate RIS_gravity_inversion
Install the local project
make install
If you get errors related to the PyProj EPSG database, try the following:
mamba install -c conda-forge proj-data --force-reinstall -y
or
conda remove --force pyproj -y
pip install pyproj --force-reinstall
Run the tests to make sure the installation worked correctly:
make test
The various Jupyter notebooks and README files in the folder notebooks should explain how to use this inversion.