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ZENODO
Software . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Software . 2025
License: CC BY
Data sources: Datacite
ZENODO
Software . 2025
License: CC BY
Data sources: Datacite
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TerraceM-3: Marine terrace mapping using machine learning and satellite altimetry

Authors: Jara Muñoz, Julius; Mey, Jürgen; Freisleben, Roland; Melnick, Daniel; Markus, Weiss; Winckler, Patricio; Mavoungou, Chrystelle; +1 Authors

TerraceM-3: Marine terrace mapping using machine learning and satellite altimetry

Abstract

Marine terraces record past sea levels and serve as strain markers to quantify vertical deformation from tectonic and climatic processes. Accurate mapping of these ephemeral features is essential but often limited by data quality and operator subjectivity. TerraceM-3 reduces both non-systematic and systematic mapping errors by integrating machine learning that replicates expert interpretation within standardized workflows. A new TerraceM-ICESat module enables global, vegetation-free, high-resolution mapping using ICESat-2 altimetry, including shallow offshore bathymetry. Tested in Peru and Algeria, TerraceM-3 reveals detailed coastal deformation patterns and advances research in tectonic geomorphology and coastal hazard assessment. Financial support: This study was supported by TANTA “Earthquakes and coastal deformation in subduction zones at continental scale” grant P2022-13-001 funded by the Carl-Zeiss-Stiftung; the Millennium Nucleus CYCLO “The Seismic Cycle Along Subduction Zones” grant NC160025 funded by the Millennium Scientific Initiative (ICM) of the Chilean Government; the Chilean National Fund for Development of Science and Technology (FONDECYT) grant 1150321; and the German Science Foundation (DFG) grant STR373/41-1.

Keywords

TerraceM, Marine terraces, Tectonics, Geomorphology, Seismotectonics, Tectonic geomorphology

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average