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Controls on continental shelf width: A machine learning approach
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DOI：10.1016/j.geomorph.2023.108729.png)
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The continental shelf edge marks the transition between shallow- and deep-water environments, and records the cumulative influence of subaerial and submarine processes. The width of the shelf therefore dictates where, and how efficiently, particulates are transferred into the deep-ocean. Previous studies have shown that shelf width is heavily influenced by eustasy and tectonics through geological time, however the dominant subaerial and submarine controls on shelf width during the present-day highstand remains unquantified on a continental scale. Using 15 climatic, hydrographic, oceanographic and tectonic variables, coupled with machine learning algorithms, we assess the dominant predictors of shelf width along 51,000 km of the continental margin of the Americas. Results show that the thickness of sediment accumulated on the shelf and the seismicity of the nearest onshore catchment best predict shelf width, with thinner sediment accumulations and more seismically-active catchments resulting in narrower shelves. Tectonics is therefore a first order predictor of shelf width, with the narrowest shelves associated with tectonically-active, steep and low-accommodation margins. These findings support the view that tectonics is the dominant control on shelf width variation across the Earth's surface in the present-day, and indicate that tectonics will modify the width of the shelf during all sea-level stands.
Keyword:
Continental shelf
Machine learning
Random forest
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3.3
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8.6K
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2.8W
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