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Kristian Gundersen

University of Bergen - Department of Mathematics

P. O. Box 7803

Realfagbygget, Allégt. 41

Bergen, N-5020

Norway

  • University of Bergen - Department of Mathematics

SCHOLARLY PAPERS

4

DOWNLOADS

494

TOTAL CITATIONS

7

Scholarly Papers (4)

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1.

Ensuring Efficient and Robust Offshore Storage - Use of Models and Machine Learning Techniques to Design Leak Detection Monitoring

14th Greenhouse Gas Control Technologies Conference Melbourne 21-26 October 2018 (GHGT-14)
Number of pages: 11 Posted: 04 Apr 2019 Last Revised: 27 Oct 2020
Kristian Gundersen, Anna Oleynik, Guttorm Alendal, Hans Skaug, Helge Avlesen, Jarle Berntsen, Nello Blaser, Jeremy Blackford and Pierre Cazenave
University of Bergen - Department of Mathematics, University of Bergen - Department of Mathematics, University of Bergen - Department of Mathematics, University of Bergen, Bjerknes Centre for Climate Research, University of Bergen - Department of Mathematics, University of Bergen - Department of Mathematics, Plymouth Marine Laboratory and Plymouth Marine Laboratory
Downloads 151 (497,070)
Citation 1

Abstract:

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Geochemical modelling, GHGT-14, Convolutional neural networks, Time series classification, CO2 leak detection, CCS, Marine Monitoring

2.

Ensuring Efficient and Robust Offshore Storage – The Role of Marine System Modelling

14th Greenhouse Gas Control Technologies Conference Melbourne 21-26 October 2018 (GHGT-14)
Number of pages: 10 Posted: 04 Apr 2019 Last Revised: 27 Oct 2020
Jerry Blackford, Guttorm Alendal, Yuri Artioli, Helge Avlesen, Pierre Cazenave, Baixin Chen, Andy Dale, Marius Dewar, Maribel I. García-Ibáñez, Jonas Gros, Kristian Gundersen, Matthias Haeckel, Sorush Khajepor, Gennadi Lessin, Anna Oleynik, Abdirahman M. Omar and Umer Saleem
Plymouth Marine Laboratory, University of Bergen - Department of Mathematics, Plymouth Marine Laboratory, Bjerknes Centre for Climate Research, Plymouth Marine Laboratory, Heriot-Watt University, GEOMAR Helmholtz Centre for Ocean Research Kiel, Heriot-Watt University, Bjerknes Centre for Climate Research, GEOMAR Helmholtz Centre for Ocean Research Kiel, University of Bergen - Department of Mathematics, GEOMAR Helmholtz Centre for Ocean Research Kiel, Heriot-Watt University, Plymouth Marine Laboratory, University of Bergen - Department of Mathematics, Bjerknes Centre for Climate Research and Heriot-Watt University
Downloads 139 (530,816)
Citation 4

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Monitoring: geochemical methods, GHGT-14

3.

Application of Deep Learning for Characterization of CO2 Leakage Based on Above Zone Monitoring Interval (AZMI) Pressure Data

Proceedings of the 15th Greenhouse Gas Control Technologies Conference 15-18 March 2021
Number of pages: 5 Posted: 05 Apr 2021
Kristian Gundersen, Seyyed Hosseini, Anna Oleynik and Guttorm Alendal
University of Bergen - Department of Mathematics, University of Texas at Austin - Gulf Coast Carbon Center, University of Bergen - Department of Mathematics and University of Bergen - Department of Mathematics
Downloads 103 (684,672)

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AZMI; Pressure Reconstruction; Classification; Bayesian Variational Methods, Semi Conditional Variational Auto-Encoders; Multi tasks Learning; CCS Monitoring

4.

Simplified Modelling as a Tool to Locate and Quantify Fluxes from a CO2 Seep to Marine Waters

14th Greenhouse Gas Control Technologies Conference Melbourne 21-26 October 2018 (GHGT-14)
Number of pages: 7 Posted: 04 Apr 2019 Last Revised: 27 Oct 2020
Anna Oleynik, Kristian Gundersen, Guttorm Alendal, Hans Skaug, Mårten Gullliksson, Helge Avlesen, Jarle Berntsen, Jeremy Blackford and Pierre Cazenave
University of Bergen - Department of Mathematics, University of Bergen - Department of Mathematics, University of Bergen - Department of Mathematics, University of Bergen, Örebro University - School of Science and Technology, Bjerknes Centre for Climate Research, University of Bergen - Department of Mathematics, Plymouth Marine Laboratory and Plymouth Marine Laboratory
Downloads 101 (684,672)
Citation 2

Abstract:

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Geochemical modelling, GHGT-14, CO2 seep localization, CCS, marine monitoring, inverse problems, Bayesian methods