Coastal to Abyssal Vertical Sediment Accumulation Rates Predicted via Machine-Learning: Towards Sediment Characterization on a Global Scale

Observed vertical sediment accumulation rates (SARs; n = 1166) 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 contemporary vertical SARs. Rates were converted to cm yr^-1, paired with the observation’s longitude and latitude, and placed into a machine-learning based Geospatial Predictive Seafloor Model (GPSM). GPSM finds correlations between the data and established global “predictors” (quantities known or estimable everywhere; e.g. distance from coast line, river mouths, etc.).  The result, using a k-nearest neighbor (k-NN) algorithm, is a 5-arc-minute global map of predicted vertical SARs.  The map generated provides a global reference for vertical sedimentation from coastal to abyssal depths.  Areas of highest sedimentation, ~3-8 cm yr^-1, are generally river mouth proximal coastal zones and continental shelves on passive tectonic margins (e.g. the Gulf of Mexico, eastern United States, eastern continental Asia, the Pacific Islands north of Australia), with rates falling exponentially towards the deepest parts of the oceans.  Coastal zones on active tectonic margins display vertical sedimentation of ~1 cm yr^-1, which is limited to near shore when compared to passive margins.  Abyssal depth rates are functionally zero at the time scale examined (~10^-4 cm yr^-1), and increase one order of magnitude near the Mid-Atlantic ridge and at the conjunction of the Pacific, Nazca, and Cocos tectonic plates.  Predicted sedimentation patterns are then compared to established quantities of fluvial sediment discharge to the oceans, calculated by Milliman and Farnsworth in River Discharge to the Coastal Ocean: A Global Synthesis (2011).