A Machine Learning (KNN) Approach to Predicting Global Seafloor Total Organic Carbon
Journal Article - Open Access
NAVAL RESEARCH LAB WASHINGTON DC WASHINGTON United States
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Seafloor properties, including total organic carbon TOC, are sparsely measured on a global scale, and interpolation prediction techniques are often used as a proxy for observation. Previous geospatial interpolations of seafloor TOC exhibit gaps where little to no observed data exists. In contrast, recent machine learning techniques, relying on geophysical and geochemical properties e.g., seafloor biomass, porosity, and distance from coast, show promise in making comprehensive, statistically optimal predictions. Here we apply a nonparametric i.e., data-driven machine learning algorithm, specifically k-nearest neighbors kNN, to estimate the global distribution of seafloor TOC. Our results include predictor feature selection specifically designed to mitigate bias and produce a statistically optimal estimation of seafloor TOC, with uncertainty, at 5 x 5-arc minute resolution. Analysis of parameter space sample density provides a guide for future sampling. One use for this prediction is to constrain a global inventory, indicating that just the upper 5 cm of the seafloor contains about 87 - 43 gigatons of carbon Gt C in organic form.