Pipeline behind a global catalogue of bathymetric features (seamounts, knolls, ridges) from GEBCO grids using prominence and isobath analysis. Published in Scientific Data (Nature); data on PANGAEA (doi:10.1594/PANGAEA.992546); explore via BathyCat.
ProboSed: Bridging legacy core logs and modern machine learning. An open-source suite for probabilistic sediment characterization and nonlinear failure analysis in subduction margins.
Geomorphometric delineation of physiographic domains at Pacific subduction margins: axis-relative attribute stacks, unsupervised clustering, and reproducible figures.
Code and derived data for the Pacific trench-fill morphology paper
GMT (Generic Mapping Tools) shell scripts mapping total marine sediment thickness from the NOAA/NGDC GlobSed grid over ocean basins, seas and trenches, with turbo colour-shaded grids, isopach contours, coastlines and cartographic layout.
Comprehensive comparative geomorphometric analysis of the deep-sea trenches of the Pacific Ocean from bathymetric data, using data-modelling algorithms in GMT, QGIS, Python and R. LaTeX source together with the GMT/AWK, Python and R scripts and all figures.
LaTeX source of the scientific report 'Morphostructural features of the deep-sea trenches in the Pacific Ocean, the problem of their origin' (P. Lemenkova, Moscow, 2021, 268 pp.).
LaTeX (Beamer) source for the presentation 'Scatterplot Matrices of the Geomorphic Structure of the Mariana Trench at Four Tectonic Plates (Pacific, Philippine, Mariana and Caroline): A Geostatistical Analysis by R' (Lemenkova), talk given 2019-02-01 at the 51st Tectonics Meeting, Lomonosov Moscow State University / Institute of Geology RAS.
Reproducible Python/PyGMT workflow testing the geomorphic imprint of subducting seamounts and ridges on Pacific trenches — axial-depth anomaly, inner-slope scarring and along-strike segmentation — from open bathymetry with statistics and a random-forest classifier.
Reproducible Python/pyGMT pipeline quantifying the sensitivity of deep-sea trench morphometry to the choice of global bathymetric grid (GEBCO, SRTM15+, GMRT).
KUNLING: Knowledge-augmented Universal Neural Learning for Intelligent Numerical Geoscience. By Dr. Ze Liu (OUC). A core framework for multi-scale spheres coupling, intelligent data assimilation, and deep-time reconstruction. Bridging tectonic-climate-sedimentary modeling with high-fidelity paleogeomorphology and paleoenvironment inversion.
To associate your repository with the marine-geology topic, visit your repo's landing page and select "manage topics."