Beazley, M. J.: The significance of organic carbon and sediment surface area to the benthic biogeochemistry of the slope and deep water environments of the northern Gulf of Mexico, Master's thesis, Texas A&M University,
http://hdl.handle.net/1969.1/534 (last access: 3 February 2024), 2003.
a,
b
Berner, R. A.: Burial of organic carbon and pyrite sulfur in the modern ocean: its geochemical and environmental significance, Am. J. Sci., 282, 451–473,
https://doi.org/10.2475/ajs.282.4.451, 1982.
a,
b,
c,
d,
e,
f,
g,
h,
i,
j
Berner, R. A.: Processes of the Long-Term Carbon Cycle: Organic Matter and Carbonate Burial and Weathering, in: The Phanerozoic Carbon Cycle: CO
2 and O
2, Oxford University Press, ISBN 9780195173338,
https://doi.org/10.1093/oso/9780195173338.003.0005, 2004.
a
Boyer, T. P., Antonov, J. I., Baranova, O. K., Coleman, C., Garcia, H. E., and Grodsky, A.: World Ocean Database 2013, NOAA Atlas NESDIS 72, Technical Ed. Silver Spring, MD,
https://doi.org/10.7289/V5NZ85MT, 2013.
a,
b,
c,
d,
e,
f,
g,
h,
i,
j,
k,
l
Bradley, J. A., Hülse, D., LaRowe, D. E., and Arndt, S.: Transfer efficiency of organic carbon in marine sediments, Nat. Commun., 13, 7297,
https://doi.org/10.1038/s41467-022-35112-9, 2022.
a,
b,
c
Burdige, D. J.: Preservation of Organic Matter in Marine Sediments – Controls, Mechanisms, and an Imbalance in Sediment Organic Carbon Budgets?, Chem. Rev., 107, 467–485,
https://doi.org/10.1021/cr050347q, 2007.
a
De Veaux, R. D. and Ungar, L. H.: Multicollinearity: A tale of two nonparametric regressions, in: Selecting Models from Data, edited by: Cheeseman, P. and Oldford, R. W., Springer New York, New York, NY, 393–402, ISBN 978-1-4612-2660-4, 1994. a
Diesing, M., Thorsnes, T., and Bjarnadóttir, L. R.: Organic carbon densities and accumulation rates in surface sediments of the North Sea and Skagerrak, Biogeosciences, 18, 2139–2160,
https://doi.org/10.5194/bg-18-2139-2021, 2021.
a,
b
Emerson, S. and Hedges, J. I.: Processes controlling the organic carbon content of open ocean sediments, Paleoceanography, 3, 621–634,
https://doi.org/10.1029/PA003i005p00621, 1988.
a,
b,
c,
d
Emery, K. O.: Relict sediments on continental shelves of the world, Am. Assoc. Petr. Geol. B., 52, 445–464, 1968.
a,
b
Flanders Marine Institute: Global Oceans and Seas, version 1,
https://doi.org/10.14284/542,
https://www.marineregions.org/ (last access: 25 August 2023), 2021.
a
Gal, Y. and Ghahramani, Z.: Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning, in: Proceedings of The 33rd International Conference on Machine Learning, New York, New York, USA, edited by: Balcan, M. F. and Weinberger, K. Q., Proceedings of Machine Learning Research, vol. 48, PMLR, 19 June 2016, 1050–1059,
https://proceedings.mlr.press/v48/gal16.html (last access: 24 February 2022), 2016. a
Garlan, T., Gabelotaud, I., Lucas, S., and Marchès, E.: A World Map of Seabed Sediment Based on 50 Years of Knowledge, World Academy of Science, Engineering and Technology, International Journal of Geological and Environmental Engineering, 12, 409–419, 2018.
a,
b,
c,
d,
e,
f,
g,
h,
i
Goyet, C., Healy, R., and Ryan, J.: Global Distribution of Total Inorganic Carbon and Total Alkalinity Below the Deepest Winter Mixed Layer Depths, ORNIJCDIAC-127 NDP-076,
https://doi.org/10.3334/zCDIAC/otg.ndp076, 2000.
a
Hall, S. J.: The continental shelf benthic ecosystem: current status, agents for change and future prospects, Environ. Conserv., 29, 350–374,
http://www.jstor.org/stable/44520615 (last access: 26 June 2022), 2002. a
Hart-Davis, M. G., Piccioni, G., Dettmering, D., Schwatke, C., Passaro, M., and Seitz, F.: EOT20: a global ocean tide model from multi-mission satellite altimetry, Earth Syst. Sci. Data, 13, 3869–3884,
https://doi.org/10.5194/essd-13-3869-2021, 2021.
a,
b,
c,
d,
e
He, K., Zhang, X., Ren, S., and Sun, J.: Delving deep into rectifiers: Surpassing human-level performance on imagenet classification, in: Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile, 1026–1034, 7–13 December 2015. a
Hedges, J. I. and Keil, R. G.: Sedimentary organic matter preservation: an assessment and speculative synthesis, Mar. Chem., 49, 81–115,
https://doi.org/10.1016/0304-4203(95)00008-F, 1995.
a,
b,
c,
d
Jørgensen, B. B., Wenzhöfer, F., Egger, M., and Glud, R. N.: Sediment oxygen consumption: Role in the global marine carbon cycle, Earth-Sci. Rev., 228, 103987,
https://doi.org/10.1016/j.earscirev.2022.103987, 2022.
a,
b,
c,
d
Kullback, S. and Leibler, R. A.: On Information and Sufficiency, Ann. Math. Stat., 22, 79–86,
http://www.jstor.org/stable/2236703 (last access: 19 April 2023), 1951. a
LaRowe, D., Arndt, S., Bradley, J., Estes, E., Hoarfrost, A., Lang, S., Lloyd, K., Mahmoudi, N., Orsi, W., Shah Walter, S., Steen, A., and Zhao, R.: The fate of organic carbon in marine sediments – New insights from recent data and analysis, Earth-Sci. Rev., 204, 103146,
https://doi.org/10.1016/j.earscirev.2020.103146, 2020a.
a,
b
LaRowe, D. E., Arndt, S., Bradley, J. A., Burwicz, E., Dale, A. W., and Amend, J. P.: Organic carbon and microbial activity in marine sediments on a global scale throughout the Quaternary, Geochim. Cosmochim. Ac., 286, 227–247,
https://doi.org/10.1016/j.gca.2020.07.017, 2020b.
a
LeCun, Y., Bengio, Y., and Hinton, G.: Deep learning, Nature, 521, 436–444,
https://doi.org/10.1038/nature14539, 2015.
a
Lee, T. R., Wood, W. T., and Phrampus, B. J.: A Machine Learning (kNN) Approach to Predicting Global Seafloor Total Organic Carbon, Global Biogeochem. Cy., 33, 37–46,
https://doi.org/10.1029/2018GB005992, 2019.
a,
b,
c,
d,
e,
f,
g,
h,
i,
j,
k,
l,
m,
n,
o,
p
Lee, T. R., Wood, W. T., Skarke, A., Phrampus, B. J., and Obelcz, J.: Data files associated with the
k-nearest neighbor global prediction of isopachs for present to middle Miocene, Zenodo [data set],
https://doi.org/10.5281/zenodo.3675364, 2020.
a,
b
Legge, O., Johnson, M., Hicks, N., Jickells, T., Diesing, M., Aldridge, J., Andrews, J., Artioli, Y., Bakker, D. C. E., Burrows, M. T., Carr, N., Cripps, G., Felgate, S. L., Fernand, L., Greenwood, N., Hartman, S., Kröger, S., Lessin, G., Mahaffey, C., Mayor, D. J., Parker, R., Queirós, A. M., Shutler, J. D., Silva, T., Stahl, H., Tinker, J., Underwood, G. J. C., Van Der Molen, J., Wakelin, S., Weston, K., and Williamson, P.: Carbon on the Northwest European Shelf: Contemporary Budget and Future Influences, Frontiers in Marine Science, 7, 143,
https://doi.org/10.3389/fmars.2020.00143, 2020.
a
Lucas, M. and Giles, H.: Quantifying Uncertainty in Random Forests via Confidence Intervals and Hypothesis Tests, J. Mach. Learn. Res., 17, 1–41,
http://jmlr.org/papers/v17/14-168.html (last access: 4 August 2023), 2016. a
Ludwig, W., Amiotte-Suchet, P., and Probst, J. L.: ISLSCP II Global River Fluxes of Carbon and Sediments to the Oceans, ORNL Distributed Active Archive Center [data set],
https://doi.org/10.3334/ORNLDAAC/1028, 2011.
a,
b,
c,
d,
e
Lundberg, S. M. and Lee, S.-I.: A unified approach to interpreting model predictions, in: Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS'17, Curran Associates Inc., Red Hook, NY, USA, 4–9 December 2017, 4768–4777, ISBN 9781510860964, 2017.
a,
b
Martin, K. M., Wood, W. T., and Becker, J. J.: A global prediction of seafloor sediment porosity using machine learning, Geophys. Res. Lett., 42, 10640–10646,
https://doi.org/10.1002/2015GL065279, 2015.
a,
b,
c,
d,
e,
f,
g,
h
NASA: Announcement of Aquarius Level 2 Data Availability, Physical Oceanography Distributed Active Archive Center (PODAAC),
https://aquarius.oceansciences.org/cgi/gal_density.htm (last access: 23 October 2023), 2011.
a,
b
NASA: MODIS-Aqua Ocean Color Data, Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group,
https://doi.org/10.5067/AQUA/MODIS_OC.2014.0, 2014.
a,
b,
c,
d,
e,
f
National Geophysical Data Center: The NGDC Seafloor Sediment Grain Size Database, first Version, NOAA National Centers for Environmental Information,
https://doi.org/10.7289/V5G44N6W, 1976.
a
Pape, T., Bünz, S., Hong, W.-L., Torres, M. E., Riedel, M., Panieri, G., Lepland, A., Hsu, C.-W., Wintersteller, P., Wallmann, K., Schmidt, C., Yao, H., and Bohrmann, G.: Origin and Transformation of Light Hydrocarbons Ascending at an Active Pockmark on Vestnesa Ridge, Arctic Ocean, J. Geophys. Res.-Sol. Ea., 125, e2018JB016679,
https://doi.org/10.1029/2018JB016679, 2020.
a,
b
Paradis, S., Nakajima, K., Van der Voort, T. S., Gies, H., Wildberger, A., Blattmann, T. M., Bröder, L., and Eglinton, T. I.: The Modern Ocean Sediment Archive and Inventory of Carbon (MOSAIC): version 2.0, Earth Syst. Sci. Data, 15, 4105–4125,
https://doi.org/10.5194/essd-15-4105-2023, 2023.
a,
b
Parameswaran, N., González, E., Burwicz-Galerne, E., Braack, M., and Wallmann, K.: Code for Global Prediction Of Total Organic Carbon In Marine Sediments Using Deep Neural Networks (nn-toc), GitHub [code],
https://github.com/paramnav/nn-toc (last access: 13 November 2024), 2024a. a
Parameswaran, N., González, E., Burwicz-Galerne, E., Braack, M., and Wallmann, K.: Dataset for the Global Prediction Of Total Organic Carbon In Marine Sediments Using Deep Neural Networks (nn-toc), Zenodo [data set],
https://doi.org/10.5281/zenodo.11186224, 2024b.
a,
b,
c,
d
Pavlis, N. K., Holmes, S. A., Kenyon, S. C., and Factor, J. K.: The EGM2008 Global Gravitational Model, abstract 2008AGUFM.G22A..01P, 2008 General Assembly of the European Geosciences Union, Vienna, Austria,
https://ui.adsabs.harvard.edu/abs/2008AGUFM.G22A..01P (last access: 7 October 2014), 2008.
a,
b
Phrampus, B. J., Lee, T. R., and Wood, W. T.: Predictor Grids for “A Global Probabilistic Prediction of Cold Seeps and Associated Seafloor Fluid Expulsion Anomalies (SEAFLEAs)”, Zenodo [data set],
https://doi.org/10.5281/zenodo.3459805, 2019.
a
Rényi, A.: On measures of entropy and information, in: Proceedings of the fourth Berkeley symposium on mathematical statistics and probability, volume 1: contributions to the theory of statistics, Fourth Berkley Symposum on Mathematical Statistics and Probablity June 20-July 30, 1960, Statistical Laboratory of the University of California vol. 4, University of California Press, 547–562, 1961. a
Restreppo, G. A., Wood, W. T., and Phrampus, B. J.: Oceanic sediment accumulation rates predicted via machine learning algorithm: towards sediment characterization on a global scale, Geo-Mar. Lett., 40, 755–763,
https://doi.org/10.1007/s00367-020-00669-1, 2020.
a
Restreppo, G. A., Wood, W. T., and Phrampus, B. J.: A machine-learning derived model of seafloor sediment accumulation, Mar. Geol., 440, 106577,
https://doi.org/10.1016/j.margeo.2021.106577, 2021.
a,
b
Romankevich, E., Vetrov, A., and Peresypkin, V.: Organic matter of the World Ocean, Russ. Geol. Geophys., 50, 299–307,
https://doi.org/10.1016/j.rgg.2009.03.013, 2009.
a,
b
Sala, E., Mayorga, J., Bradley, D., Cabral, R. B., Atwood, T. B., Auber, A., Cheung, W., Costello, C., Ferretti, F., Friedl, er, A. M., Gaines, S. D., Garilao, C., Goodell, W., Halpern, B. S., Hinson, A., Kaschner, K., Kesner-Reyes, K., Leprieur, F., McGowan, J., Morgan, L. E., Mouillot, D., Palacios-Abrantes, J., Possingham, H. P., Rechberger, K. D., Worm, B., and Lubchenco, J.: Protecting the global ocean for biodiversity, food and climate, Nature, 592, 397–402,
https://doi.org/10.1038/s41586-021-03371-z, 2021.
a
Seiter, K., Hensen, C., Schröter, J., and Zabel, M.: Organic carbon content in surface sediments – defining regional provinces, Deep-Sea Res. Pt. I, 51, 2001–2026,
https://doi.org/10.1016/j.dsr.2004.06.014, 2004.
a,
b,
c,
d,
e,
f,
g
Song, S., Santos, I. R., Yu, H., Wang, F., Burnett, W. C., Bianchi, T. S., Dong, J., Lian, E., Zhao, B., Mayer, L., Yao, Q., Yu, Z., and Xu, B.: A global assessment of the mixed layer in coastal sediments and implications for carbon storage, Nat. Commun., 13, 4903,
https://doi.org/10.1038/s41467-022-32650-0, 2022.
a,
b
Song, T., Pang, C., Hou, B., Xu, G., Xue, J., Sun, H., and Meng, F.: A review of artificial intelligence in marine science, Front. Earth Sci., 11,
https://doi.org/10.3389/feart.2023.1090185, 2023.
a
The HYCOM+NCODA Ocean Reanalysis: 1/12 deg global HYCOM+NCODA Ocean Reanalysis, funded by: U.S. Navy and the Modeling and Simulation Coordination Office,
https://www.hycom.org/data/glbu0pt08/expt-19pt1 (last access: 19 March 2014), 2014.
a,
b
Thyng, K. M., Greene, C. A., Hetland, R. D., Zimmerle, H. M., and DiMarco, S. F.: True Colors of Oceanography, Oceanography, 29, 9–13,
https://doi.org/10.5670/oceanog.2016.66, 2016.
a
van der Voort, T. S., Blattmann, T. M., Usman, M., Montluçon, D., Loeffler, T., Tavagna, M. L., Gruber, N., and Eglinton, T. I.: MOSAIC (Modern Ocean Sediment Archive and Inventory of Carbon): a (radio)carbon-centric database for seafloor surficial sediments, Earth Syst. Sci. Data, 13, 2135–2146,
https://doi.org/10.5194/essd-13-2135-2021, 2021.
a,
b
Wei, C.-L., Rowe, G. T., Escobar-Briones, E., Boetius, A., Soltwedel, T., and Caley, M. J.: Global patterns and predictions of seafloor biomass using random forests, PLoS ONE, 5, e15323,
https://doi.org/10.1371/journal.pone.0015323, 2010.
a,
b
Whittaker, J., Goncharov, A., Williams, S., Müller, R. D., and Leitchenkov, G.: Global sediment thickness dataset updated for the Australian-Antarctic Southern Ocean, Geochem. Geophy. Geosy., 14, 3297–3305,
https://doi.org/10.1002/ggge.20181, 2013.
a
