TerraceM 3.0: Advancing marine terrace mapping through integrated machine learning methods
- 1Hochschule Biberach, Institute of geology and environmental research, Faculty of Civil Engineering, Biberach an der Riß, Germany (jara@geo.uni-potsdam.de)
- 2University of Potsdam, Institute of Environmental research and geography, Karl-Liebknecht Straße 24-25, Golm, Germany
- 3University of Potsdam, Institute of Geosciences, Karl-Liebknecht Straße 24-25, Golm, Germany
- 44. University Caen, Unirouen, CNRS, M2C, 14 000 Caen, France
- 55. Universidad Austral, Instituto de Ciencias de la Tierra TAQUACh, Edificio Emilio Pugín, Av. Eduardo Morales Miranda, Campus Isla Teja, Valdivia, Chile
TerraceM is an open-source software written in MATLAB for mapping and analyzing marine terraces. In this latest release, TerraceM-3 has undergone significant evolution, which leverages the capabilities of machine learning to introduce an automated marine terrace mapping feature. This new version includes a neural network that has been meticulously trained with over 1000 mapped marine terraces. This allows TerraceM-3 users to effortlessly map marine terraces and precisely determine their elevation through the automated mapping of their shoreline angles. In addition, TerraceM-3 incorporates two new functionalities: 1) Photon profile mapping, which includes mapping of satellite LiDAR profiles from the IceSat-2 mission, which broadens the applicability of TerraceM-3 beyond the availability of topographic data. 2) Indicative meaning calculator that accounts for the factors that can alter the initial sea-level position using global datasets (wave conditions and tidal ranges). This method facilitates the direct assessment of uncertainties in the reconstructions of the paleo-sea-level based on marine terraces. TerraceM-3 is a complete toolkit for researchers and students engaged in marine terrace analysis by offering a unique blend of numerical methods, statistical analyses techniques and additional enhanced functionalities to precisely map marine terraces and using them as markers of tectonic deformation.
How to cite: Jara Muñoz, J., Mey, J., Freisleben, R., Pedoja, K., and Melnick, D.: TerraceM 3.0: Advancing marine terrace mapping through integrated machine learning methods, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5716, https://doi.org/10.5194/egusphere-egu24-5716, 2024.