Datasets:

NoraResearchLab
/

gplates-tectonic-intelligence

license: cc-by-4.0
pretty_name: GPlates Tectonic Intelligence Dataset
task_categories:
  - other
  - feature-extraction
tags:
  - geoscience
  - geology
  - tectonics
  - plate-tectonics
  - geospatial
  - gis
  - geoai
  - remote-sensing
  - earth-observation
  - earth-science
language:
  - en

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GPlates Tectonic Intelligence Dataset

Overview

GPlates Tectonic Intelligence Dataset is a standardized geospatial dataset maintained by NORA Research Lab, converting the Müller et al. (2019) global plate reconstruction model into AI-ready spatial features.

Every cell in a 1.0° global grid (360 × 180 = 64,800 points, present day / 0 Ma) is tagged with its reconstruction plate ID, seafloor age, and distance to the nearest mid-ocean ridge, subduction trench, transform fault, and coastline — turning raw tectonic geometry into tabular features for geological, geophysical, exploration, and environmental ML applications.

Quick Links

GitHub: https://github.com/Nora-Research-Lab
Hugging Face: https://huggingface.co/NoraResearchLab
Dataset: https://huggingface.co/datasets/NoraResearchLab/gplates-tectonic-intelligence
LinkedIn: https://www.linkedin.com/company/nora-research-lab
X: https://x.com/noraresearchlab

Dataset Structure

Format: GeoParquet (WGS84 / EPSG:4326), one row per grid cell.

Field	Type	Description
`lon`	float	Cell center longitude (degrees)
`lat`	float	Cell center latitude (degrees)
`plate_id`	int	Reconstruction plate ID (Müller et al. 2019 model)
`age_ma`	float	Seafloor age in millions of years; null over continental crust
`dist_to_ridge_km`	float	Distance to nearest mid-ocean ridge segment
`dist_to_trench_km`	float	Distance to nearest subduction trench
`dist_to_transform_km`	float	Distance to nearest transform fault
`dist_to_coastline_km`	float	Distance to nearest coastline

Source Data

Plate model: Müller, R.D. et al. (2019), global full-plate motion and plate boundary model.
Seafloor age grid: Seton, M. et al. (2020), present-day oceanic crustal age and spreading parameters.
Access layer: gplately (Mather et al.), built on pygplates.

Processing Pipeline

Downloaded core reconstruction files (rotation model, topologies, static polygons) and supporting layers (coastlines, continents, COBs, age grid) via gplately.DataServer.
Converted vector layers to standardized GeoJSON and GeoParquet.
Extracted labeled plate-boundary geometries (ridges, trenches, transforms) and coastlines as spatial features.
Built a global 1.0° grid, assigned plate IDs via point-in-polygon partitioning, sampled seafloor age, and computed nearest-boundary distances (reprojected to an equidistant CRS for real-world km).
Published the standardized dataset here.

Data Format

Vector datasets stored as GeoParquet. Standard coordinate reference system: EPSG:4326 (WGS84).

Attribution

All third-party scientific datasets remain the property of their original creators (EarthByte / GPlates). NORA Research Lab performs aggregation, feature generation, format conversion, metadata generation, and cloud distribution.

Users should cite both the original dataset providers and NORA Research Lab where appropriate.

Licensing

This repository includes an MIT LICENSE file for repository code, scripts, and documentation created by NORA Research Lab. The underlying plate model and age grid retain the licenses of their original providers and are not relicensed under MIT.

Intended Use

This repository is intended for:

plate tectonics and structural geology research
geospatial analysis and GIS
machine learning / GeoAI
mineral and resource exploration
academic research and data engineering

Limitations

1.0° resolution is coarse; finer grids (e.g. 0.25°) trade runtime for detail.
Bilinear interpolation on the age grid can return null values near coastlines even over oceanic crust, slightly undercounting true oceanic coverage.
Distances are computed on an equidistant cylindrical projection — accurate at global scale, with mild distortion near the poles.
Present-day (0 Ma) snapshot only; no time-series reconstruction yet.

Citation

If you use this dataset, please cite:

Müller, R.D. et al. (2019), Tectonics, 38(6).
Seton, M. et al. (2020), Geochemistry, Geophysics, Geosystems, 21(10).
Mather, B. et al., GPlately (GitHub: GPlates/gplately).
NORA Research Lab

Maintainer

NORA Research Lab

GitHub: https://github.com/Nora-Research-Lab

Hugging Face: https://huggingface.co/NoraResearchLab

Dataset: https://huggingface.co/datasets/NoraResearchLab/gplates-tectonic-intelligence

Last updated: 2026