Package: slabdip
Predict slab dipPredict the dip angle of subducting oceanic lithosphere using simple plate kinematic parameters.CiteMatheret al. (2022) "Kimberlite eruptions driven by slab flux and subduction angle".Scientific Reports. (in review)DependenciesTo run the Jupyter notebooks some dependencies are required:pygplatesgplatelyPlateTectonicToolsScikit-Learncartopy(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 arehere. While these have been written with the Mac M1 architecture in mind, the same instructions should apply equally to other distributions.InstallationMost 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 ofslabdipand all of its dependencies using conda.
This is the preferred method to installslabdipwhich downloads binaries from the conda-forge channel.condainstall-cconda-forgegplatelyCreating a new conda environmentWe recommend creating a new conda environment inside which to installslabdip. This avoids any potential conflicts in your base Python environment. In the example below we create a new environment called "my-env":condacreate-nmy-env
condaactivatemy-env
condainstall-cconda-forgeslabdipmy-envneeds to be activated whenever you useGPlately: i.e.conda activate my-env.2. Using pipFrom the current directory, runpipinstall.You can also install the most up-to-date version by runningpipinstallgit+https://github.com/brmather/Slab-Dip.gitwhich will clone themainbranch and install the latest version.Data packagesPlate reconstruction and corresponding age grids of the seafloor are required to predict slab dip. These may be downloaded fromhttps://www.earthbyte.org/gplates-2-3-software-and-data-sets/The slab dip prediction tool has been tested onClennettet al.(2020)andMülleret al.(2019)plate reconstructions but should also work fine for all other plate reconstructions.UsageA series of Jupyter notebooks document the workflow to calculate plate kinematic and rheological information used to predict slab dip. Skip tonotebook 6to jump straight into the slab dip estimator. The Python snippet below outlines the usage of theSlabDipperobject 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 modelgdownload=gplately.download.DataServer("Clennett2020")rotation_model,topology_features,static_polygons=gdownload.get_plate_reconstruction_files()# Use the PlateReconstruction object to create a plate motion modelmodel=gplately.PlateReconstruction(rotation_model,topology_features,static_polygons)# Initialise SlabDipper objectdipper=SlabDipper()dipper.model=model# Set the filename (including path) of the seafloor age and spreading rate gridsdipper.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)ReferencesClennett, 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/10.1029/2020GC009117Müller, 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