Oceanic sediment accumulation rates predicted via machine learning algorithm: towards sediment characterization on a global scale

Abstract

AbstractObserved vertical sediment accumulation rates (n = 1031) were gathered from ~ 55 years of peer reviewed literature. Original methods of rate calculation include long-term isotope geochronology (14C, 210Pb, and 137Cs), pollen analysis, horizon markers, and box coring. These observations are used to create a database of global, contemporary vertical sediment accumulation rates. Rates were converted to cm year−1, paired with the observation’s longitude and latitude, and placed into a machine learning–based Global Predictive Seabed Model (GPSM). GPSM finds correlations between the data and established global “predictors” (quantities known or estimable everywhere, e.g., distance from coastline and river mouths). The result, using a k-nearest neighbor (k-NN) algorithm, is a 5-arc-minute global map of predicted benthic vertical sediment accumulation rates. The map generated provides a global reference for vertical sedimentation from coastal to abyssal depths. Areas of highest sedimentation, ~ 3–8 cm year−1, are generally river mouth proximal coastal zones draining relatively large areas with high maximum elevations and with wide, shallow continental shelves (e.g., the Gulf of Mexico and the Amazon Delta), with rates falling exponentially towards the deepest parts of the oceans. The exception is Oceania, which displays significant vertical sedimentation over a large area without draining the large drainage basins seen in other regions. Coastal zones with relatively small drainage basins and steep shelves display vertical sedimentation of ~ 1 cm year−1, which is limited to the near shore when compared with shallow, wide margins (e.g., the western coasts of North and South America). Abyssal depth rates are functionally zero at the time scale examined (~ 10−4 cm year−1) and increase one order of magnitude near the Mid-Atlantic Ridge and at the Galapagos Triple Junction.