[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123993-en":3,"doc-seo-123993-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123993,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Predicting particle catchment areas of deep-ocean sediment traps using machine learning","The biological carbon pump governs key climate and biogeochemical cycles by exporting photosynthetically produced particles from the surface to the deep ocean. Deep-ocean sediment traps quantify carbon fluxes but make it difficult to determine where trapped particles originated because circulation alters their sinking trajectories over thousands of meters. Numerical Lagrangian experiments in the North Atlantic Porcupine Abyssal Plain are used to train a machine learning method that predicts surface source areas from surface conditions. Results show predictive value from low kinetic energy and mesoscale eddies above the trap, supporting integration with satellite observations.","Ocean Sci., 20, 1149–1165, 2024  \n[https://doi.org/10.5194/os-20-1149-2024](https://doi.org/10.5194/os-20-1149-2024)[ ](https://doi.org/10.5194/os-20-1149-2024)© Author(s) 2024 . This work is distributed under the Creative Commons Attribution 4 .0 License.  \nPredicting particle catchment areas of deep-ocean sediment traps using machine learning  \nThéo Picard 1 , Jonathan Gula2,3,5 , Ronan Fablet4,5 , Jeremy Collin 1 , and Laurent Mémery 1  \n1Laboratoire des Sciences de l'Environnement Marin (LEMAR), Univ Brest, CNRS, IRD, Ifremer, IUEM, Plouzané, France  \n2Laboratoire d'Océanographie Physique et Spatiale (LOPS), Univ Brest, CNRS, IRD, Ifremer, IUEM, Plouzané, France  \n3Institut Universitaire de France (IUF), Paris, France  \n4IMT Atlantique, Lab-STICC, Plouzané, France  \n5 ODYSSEY, Inria, Brest, France  \nCorrespondence: Théo Picard ([theo.picard@univ-brest.fr](theo.picard@univ-brest.fr))  \nReceived: 23 November 2023 – Discussion started: 5 December 2023  \nRevised: 31 May 2024 – Accepted: 22 July 2024 – Published: 19 September 2024  \nAbstract. The ocean's biological carbon pump plays a major role in climate and biogeochemical cycles. Photosynthesis at the surface produces particles that are exported to the deep ocean by gravity. Sediment traps, which measure deepcarbon fluxes, help to quantify the carbon stored by this process. However, it is challenging to precisely identify the surface origin of particles trapped thousands of meters deep due to the influence of ocean circulation on the sinking path of carbon. In this study, we conducted a series of numerical Lagrangian experiments in the Porcupine Abyssal Plain region of the North Atlantic and developed a machine learning approach to predict the surface origin of particles trapped in a deep-ocean sediment trap. Our numerical experiments support the predictive performance of the machine learning approach, and surface conditions appear to provide valuable information for accurately predicting the source area, suggesting a potential application with satellite data. We also identify factors that potentially affect prediction efficiency, and we show that the best predictions are associated with low kinetic energy and the presence of mesoscale eddies above the trap. This new tool could provide a better link between satellite-derived sea surface observations and deepocean sediment trap measurements, ultimately improving our understanding of the biological-carbon-pump mechanism.  \n1 Introduction  \nThe biological carbon pump (BCP) plays a major role in climate and biogeochemical cycles. The BCP reduces unperturbed atmospheric CO 2 by 35 % to 50 %(Williams and Follows, 2011) by exporting organic matter to the deep ocean, thereby supporting abyssal food webs (Billett et al., 1983 ; Rembauville et al., 2018) . The BCP is driven by photosynthesis that occurs within the euphotic layer, typically between 0 and 200 m, producing gravitationally sinking particulate organic carbon (POC) . Sinking particles have a wide range of vertical velocities, from neutral buoyancy to more than 600 m d􀀀1 (Villa-Alfageme et al., 2016), and are usually considered the main contributors to the BCP (Armstrong et al., 2001 ; Alonso-González et al., 2010 ; Siegel et al., 2014 ; Le Moigne, 2019) . While most POC is remineralized in theeuphotic and mesopelagic zones (200–1000 m), a small but significant fraction of POC reaches the deep ocean below 1000 m, where it is sequestered for hundreds or thousands of years (Lampitt et al., 2008 ; Burd et al., 2016) . Despite its critical importance, the annual estimate of global carbon export remains poorly constrained, ranging from 5 to over 12 Pg Cyr􀀀1 (Turner, 2015) . Therefore, quantifying the biological carbon pump is key to understanding the global carbon cycle and how the BCP will respond to climate change (Passow and Carlson, 2012 ; Henson et al., 2022) .  \nPublished by Copernicus Publications on behalf of the European Geosciences Union.  \n1150 T. Picard et al.: Predicting","cbCaibWuCj0nyuNY","https://ap.wps.com/l/cbCaibWuCj0nyuNY","pdf",16057699,1,17,"English","en",105,"# Introduction\n## Biological carbon pump and the need for catchment areas\n## Sediment traps, particle sinking, and circulation effects\n## Prior backtracking approaches and 3D reconstruction attempts\n## Study objective and machine learning prediction framework","[{\"question\":\"Why is predicting particle catchment areas for deep-ocean sediment traps challenging?\",\"answer\":\"Sediment traps measure deep carbon fluxes, but ocean circulation strongly reshapes particle sinking paths, spreading likely origins over large surface domains.\"},{\"question\":\"How does the study approach the prediction problem?\",\"answer\":\"The study runs numerical Lagrangian experiments and develops a machine learning method to predict the surface origin of particles captured by a deep-ocean sediment trap.\"},{\"question\":\"Which factors improve prediction efficiency in the proposed model?\",\"answer\":\"Best predictions are linked to low kinetic energy and the presence of mesoscale eddies above the trap, while the study also identifies other potentially influential factors.\"}]","Predicting particle catchment areas of deep-ocean sediment traps using machine learning | 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is predicting particle catchment areas for deep-ocean sediment traps challenging?","Question",{"text":75,"@type":76},"Sediment traps measure deep carbon fluxes, but ocean circulation strongly reshapes particle sinking paths, spreading likely origins over large surface domains.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study approach the prediction problem?",{"text":80,"@type":76},"The study runs numerical Lagrangian experiments and develops a machine learning method to predict the surface origin of particles captured by a deep-ocean sediment trap.",{"name":82,"@type":73,"acceptedAnswer":83},"Which factors improve prediction efficiency in the proposed model?",{"text":84,"@type":76},"Best predictions are linked to low kinetic energy and the presence of mesoscale eddies above the trap, while the study also identifies other potentially influential 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