slabdip 4.1.3

Method to calculate slab dip using simple plate kinematic parameters

Predict slab dip

Predict the dip angle of subducting oceanic lithosphere using simple plate kinematic parameters.

Cite

Mather et al. (2022) "Kimberlite eruptions driven by slab flux and subduction angle". Scientific Reports. (in review)

Dependencies

To run the Jupyter notebooks some dependencies are required:

• pygplates
• gplately
• PlateTectonicTools
• Scikit-Learn
• cartopy (for mapping)
• netCDF4 (to extract age grids of the seafloor)

Instructions to install these dependencies can be found within each package above. Some conda instructions for setting up a Python environment are here. While these have been written with the Mac M1 architecture in mind, the same instructions should apply equally to other distributions.

Installation

Most of the Jupyter notebooks can be run without installing this package, however, following these installation instructions will make the slab dip prediction tool available system-wide.

1. Using conda (recommended)

You can install the latest stable public release of slabdip and all of its dependencies using conda. This is the preferred method to install slabdip which downloads binaries from the conda-forge channel.

conda install -c conda-forge gplately

Creating a new conda environment

We recommend creating a new conda environment inside which to install slabdip. This avoids any potential conflicts in your base Python environment. In the example below we create a new environment called "my-env":

conda create -n my-env
conda activate my-env
conda install -c conda-forge slabdip

my-env needs to be activated whenever you use GPlately: i.e. conda activate my-env.

2. Using pip

From the current directory, run

pip install .

You can also install the most up-to-date version by running

pip install git+https://github.com/brmather/Slab-Dip.git

which will clone the main branch and install the latest version.

Data packages

Plate reconstruction and corresponding age grids of the seafloor are required to predict slab dip. These may be downloaded from https://www.earthbyte.org/gplates-2-3-software-and-data-sets/

The slab dip prediction tool has been tested on Clennett et al. (2020) and Müller et al. (2019) plate reconstructions but should also work fine for all other plate reconstructions.

Usage

A series of Jupyter notebooks document the workflow to calculate plate kinematic and rheological information used to predict slab dip. Skip to notebook 6 to jump straight into the slab dip estimator. The Python snippet below outlines the usage of the SlabDipper object which can be used with little modification to estimate slab dip for a user-defined reconstruction time.

# Call GPlately's DataServer object and download the plate model
gdownload = gplately.download.DataServer("Clennett2020")
rotation_model, topology_features, static_polygons = gdownload.get_plate_reconstruction_files()

# Use the PlateReconstruction object to create a plate motion model
model = gplately.PlateReconstruction(rotation_model, topology_features, static_polygons)

# Initialise SlabDipper object
dipper = SlabDipper()
dipper.model = model

# Set the filename (including path) of the seafloor age and spreading rate grids
dipper.set_age_grid_filename(agegrid_filename)
dipper.set_spreading_rate_grid_filename(spreadrate_filename)

# Estimate slab dip across the globe for a specified reconstruction time
# (returned as a Pandas DataFrame)
dataFrame = dipper.tessellate_slab_dip(0)

References

• Clennett, E. J., Sigloch, K., Mihalynuk, M. G., Seton, M., Henderson, M. A., Hosseini, K., et al. (2020). A Quantitative Tomotectonic Plate Reconstruction of Western North America and the Eastern Pacific Basin. Geochemistry, Geophysics, Geosystems, 21(8), 1–25. https://doi.org/10.1029/2020GC009117
• Müller, R. D., Zahirovic, S., Williams, S. E., Cannon, J., Seton, M., Bower, D. J., et al. (2019). A Global Plate Model Including Lithospheric Deformation Along Major Rifts and Orogens Since the Triassic. Tectonics, 38(6), 1884–1907. https://doi.org/10.1029/2018TC005462