• Binary time series classification with Bayesian convolutional neural networks when monitoring for marine gas discharges 

      Gundersen, Kristian; Alendal, Guttorm; Oleynik, Anna; Blaser, Nello (Journal article; Peer reviewed, 2020-06-19)
      The world’s oceans are under stress from climate change, acidification and other human activities, and the UN has declared 2021–2030 as the decade for marine science. To monitor the marine waters, with the purpose of ...
    • Detection and quantification of CO2 seepage in seawater using the stoichiometric Cseep method: Results from a recent subsea CO2 release experiment in the North Sea 

      Omar, Abdirahman; García-Ibáñez, Maribel I.; Schaap, Allison; Oleynik, Anna; Esposito, Mario; Jeansson, Emil; Loucaides, Socratis; Thomas, Helmuth; Alendal, Guttorm (Journal article; Peer reviewed, 2021)
      Carbon Capture and Storage (CCS) is a potential significant mitigation strategy to combat climate change and ocean acidification. The technology is well understood but its current implementation must be scaled up nearly ...
    • Optimal sensors placement for detecting CO2 discharges from unknown locations on the seafloor 

      Oleynik, Anna; García-Ibáñez, Maribel I.; Blaser, Nello; Omar, Abdirahman; Alendal, Guttorm (Journal article; Peer reviewed, 2020-04)
      Assurance monitoring of the marine environment is a required and intrinsic part of CO2 storage project. To reduce the costs related to the monitoring effort, the monitoring program must be designed with optimal use of ...
    • Pattern formation in a 2-population homogenized neuronal network model 

      Kolodina, Karina; Wyller, John Andreas; Oleynik, Anna; Sørensen, Mads Peter (Journal article; Peer reviewed, 2021)
      We study pattern formation in a 2-population homogenized neural field model of the Hopfield type in one spatial dimension with periodic microstructure. The connectivity functions are periodically modulated in both the ...
    • Semi-conditional variational auto-encoder for flow reconstruction and uncertainty quantification from limited observations 

      Gundersen, Kristian; Oleynik, Anna; Blaser, Nello; Alendal, Guttorm (Journal article; Peer reviewed, 2021)
      We present a new data-driven model to reconstruct nonlinear flow from spatially sparse observations. The proposed model is a version of a Conditional Variational Auto-Encoder (CVAE), which allows for probabilistic ...
    • Towards improved monitoring of offshore carbon storage: A real-world field experiment detecting a controlled sub-seafloor CO2 release 

      Flohr, Anita; Schaap, Allison; Achterberg, Eric P.; Alendal, Guttorm; Arundell, Martin; Berndt, Christian; Blackford, Jeremy; Bröttner, Christoph; Borisov, Sergey M.; Brown, Robin; Bull, Jonathan M.; Carter, Liam; Chen, Baixin; Dale, Andrew W.; De Beer, Dirk; Dean, Marcella; Deusner, Christian; Dewar, Marius; Durden, Jennifer M.; Elsen, Saskia; Esposito, Mario; Faggetter, Michael; Fischer, Jan P.; Gana, Amine; Gros, Jonas; Haeckel, Matthias; Hanz, Rudolf; Holtappels, Moritz; Hosking, Brett; Huvenne, Veerle A.I.; James, Rachael H.; Koopmans, Dirk; Kossel, Elke; Leighton, Timothy G.; Li, Jianghui; Lichtschlag, Anna; Linke, Peter; Loucaides, Socratis; Martínez-Cabanas, María; Matter, Juerg M.; Mesher, Thomas; Monk, Samuel; Mowlem, Matthew C.; Oleynik, Anna; Papadimitriou, Stathys; Paxton, David; Pearce, Christopher R.; Peel, Kate; Roche, Ben; Connelly, Douglas (Journal article; Peer reviewed, 2021)
      Carbon capture and storage (CCS) is a key technology to reduce carbon dioxide (CO2) emissions from industrial processes in a feasible, substantial, and timely manner. For geological CO2 storage to be safe, reliable, and ...