Application of machine learning to map the global distribution of deep-sea sediments

<p>The deep-sea floor accounts for >90% of seafloor area and >70% of the Earth’s surface. It acts as a receptor of the particle flux from the surface layers of the global ocean, is a place of biogeochemical cycling, records environmental and climate conditions through time and provides habitat for benthic organisms. Maps of the spatial patterns of deep-sea sediments are therefore a major prerequisite for many studies addressing aspects of deep-sea sedimentation, biogeochemistry, ecology and related fields.</p><p>A new digital map of deep-sea sediments of the global ocean is presented. The map was derived by applying the Random Forest machine-learning algorithm to published sample data of seafloor lithologies and environmental predictor variables. The selection of environmental predictors was initially based on the current understanding of the controls on the distribution of deep-sea sediments and the availability of data. A predictor variable selection process ensured that only important and uncorrelated variables were employed in the model. The three most important predictor variables were sea-surface maximum salinity, sea-floor maximum temperature and bathymetry. The occurrence probabilities of seven seafloor lithologies (Calcareous sediment, Clay, Diatom ooze, Lithogenous sediment, Mixed calcareous-siliceous ooze, Radiolarian ooze and Siliceous mud) were spatially predicted. The final map shows the most probable seafloor lithology and an associated probability value, which may be viewed as a spatially explicit measure of map confidence. An assessment of the accuracy of the map was based on a test set of observations not used for model training. Overall map accuracy was 69.5% (95% confidence interval: 67.9% - 71.1%). The sea-floor lithology map bears some resemblance with previously published hand-drawn maps in that the distribution of Calcareous sediment, Clay and Diatom ooze are very similar. Clear differences were however also noted: Most strikingly, the map presented here does not display a band of Radiolarian ooze in the equatorial Pacific.</p><p>The probability surfaces of individual seafloor lithologies, the categorical map of the seven mapped lithologies and the associated map confidence will be made freely available. It is hoped that they form a useful basis for research pertaining to deep-sea sediments.</p>

Stable Carbon and Oxygen Isotopic Composition of Mio-Pliocene Bivalve Shells and Calcareous Sediments in Nhila Anticline, Southeast Bengal Basin, Bangladesh

Stable carbon (?13C) and oxygen (?18O) isotopes of bivalve shells and calcareous sediments of the Mio-Pliocene Nhila Anticline, southeast Bengal Basin, Bangladesh have been investigated to obtain information on paleoclimate and paleoenvironment conditions during deposition. The ?13CPDB, ?18OPDB and ?18OSMOW values in bivalve shell range from -2.81‰ to -1.56‰, -3.57‰ to -2.39‰ and 27.18‰ to 28.40‰ in bivalve shells and -13.90‰ to -1.75‰, -4.71‰ to -2.13‰ and 26.01‰ to 28.66‰ in calcareous sediments, respectively. The ?13CPDB values in bivalve shells are comparable to that of calcareous sediments (~ -1.75‰) in the upper section, but ?13CPDB values in calcareous sediments are more negative excursion towards lower section (up to -13.90‰). These results signify that salinity gradients could modified the isotope values and/or strong influence of freshwater conditions. The variable ?18OPDB values in both bivalve shell and calcareous sediment suggesting diagenetic alteration of carbonates and water temperature effects. The more negative ?18OPDB values imply humid paleoclimatic conditions during the Mio-Pliocene sedimentation. Therefore, the ?18OPDB values of the Mio-Pliocene sediments of Nhila Anticline are probably controlled by paleotemperature leading to intensification of high rainfall. DOI: http://dx.doi.org/10.3329/jles.v8i0.20155 J. Life Earth Sci., Vol. 8: 113-117, 2013