by Santiago R. Soler, Agustina Pesce, Mario E. Gimenez, Leonardo Uieda

This paper has been accepted for publication in Geophysical Journal International.

An archived version of this repository is available at doi.org/10.6084/m9.figshare.8239622

We introduce a novel methodology for gravity forward modeling in spherical coordinates using tesseroids (spherical prisms) with variable densities in depth. It builds on previous work by the authors and introduces a new density-based discretization algorithm to ensure the accuracy of the numerical integration.

Application of the methodology to the Neuquén Basin, a foreland basin in the southern Andes. (a) Topography of the Neuquén Basin and its location in South America. (b) Thickness of the sedimentary basin. Inset shows the exponential density profile used in the modeling. (c) Resulting vertical gravitational acceleration at 10 km height modeled by tesseroids with exponential density variation in depth. (d) Difference between gravitational acceleration modeled using the exponential density profile and a homogeneous density.

We present a new methodology to compute the gravitational fields generated by tesseroids (spherical prisms) whose density varies with depth according to an arbitrary continuous function. It approximates the gravitational fields through the Gauss-Legendre Quadrature along with two discretization algorithms that automatically control its accuracy by adaptively dividing the tesseroid into smaller ones. The first one is a preexisting two dimensional adaptive discretization algorithm that reduces the errors due to the distance between the tesseroid and the computation point. The second is a new density-based discretization algorithm that decreases the errors introduced by the variation of the density function with depth. The amount of divisions made by each algorithm is indirectly controlled by two parameters: the distance-size ratio and the delta ratio. We have obtained analytical solutions for a spherical shell with radially variable density and compared them to the results of the numerical model for linear, exponential, and sinusoidal density functions. The heavily oscillating density functions are intended only to test the algorithm to its limits and not to emulate a real world case. These comparisons allowed us to obtain optimal values for the distance-size and delta ratios that yield an accuracy of 0.1% of the analytical solutions. The resulting optimal values of distance-size ratio for the gravitational potential and its gradient are 1 and 2.5, respectively. The density-based discretization algorithm produces no discretizations in the linear density case, but a delta ratio of 0.1 is needed for the exponential and most sinusoidal density functions. These values can be extrapolated to cover most common use cases, which are simpler than oscillating density profiles. However, the distance-size and delta ratios can be configured by the user to increase the accuracy of the results at the expense of computational speed. Lastly, we apply this new methodology to model the Neuquén Basin, a foreland basin in Argentina with a maximum depth of over 5000 m, using an exponential density function.

You can download a copy of all the files in this repository by cloning the git repository:

git clone https://github.com/pinga-lab/tesseroid-variable-density.git

or click here to download a zip archive.

All source code used to generate the results and figures in the paper are in the code folder. There you can find the Python and Cython codes that performs the gravity field calculations and scripts to generate all figures and results presented in the paper.

The sources for the manuscript text and figures are in manuscript.

See the README.md files in each directory for a full description.

You'll need a working Python 2.7 environment with all the standard scientific packages installed (numpy, scipy, matplotlib, etc). The easiest (and recommended) way to get this is to download and install the Anaconda Python distribution. Make sure you get the Python 2.7 version.

You'll also need to install version 0.5 of the Fatiando a Terra library. See the install instructions on the website.

Other dependencies needed to reproduce the results are:

• cython
• basemap
• sympy

You can install it through the conda package manager (included in Anaconda):

conda install cython basemap sympy

Instead of manually install all the dependencies, they can all be automatically installed using a conda environment.

1. Change directory to the cloned git repository:

   cd tesseroid-variable-density
   

2. Create a new conda environment from the environment.yml file:

   conda env create -f environment.yml
   

3. Activate the new enviroment:
   • Windows: activate tesseroid-variable-density
   • Linux and MacOS: source activate tesseroid-variable-density

For more information about managing conda environments visit this User Guide

The code that calculates the gravity fields generated by tesseroids with variable density can be found in code/tesseroid-density. Because it's written in Cython, you must compile it in order to call its functions. You can do it with make command:

cd tesseroid-variable-density/code/tesseroid_density
make

Windows users: It is highly recommended that you install the bash shell to run code and produce the results here. You can download bash for Windows at http://git-for-windows.github.io/. Install the "Git for Windows SDK" that will come with bash and make as well.

All source code is made available under a BSD 3-clause license. You can freely use and modify the code, without warranty, so long as you provide attribution to the authors. See LICENSE.md for the full license text.

Data and the results of numerical tests are available under the Creative Commons Attribution 4.0 License (CC-BY).

The manuscript text and figures are not open source. The authors reserve the rights to the article content, which has been accepted for publication in Geophysical Journal International.