Your browser doesn't support javascript.
loading
Numerical simulations of NMR relaxation in chalk using local Robin boundary conditions.
Ögren, M; Jha, D; Dobberschütz, S; Müter, D; Carlsson, M; Gulliksson, M; Stipp, S L S; Sørensen, H O.
Affiliation
  • Ögren M; Nano-Science Center, Department of Chemistry, University of Copenhagen, Universitetsparken 5, 2100 København Ø, Denmark; School of Science and Technology, Örebro University, 701 82 Örebro, Sweden. Electronic address: magnus@ogren.se.
  • Jha D; Nano-Science Center, Department of Chemistry, University of Copenhagen, Universitetsparken 5, 2100 København Ø, Denmark.
  • Dobberschütz S; Nano-Science Center, Department of Chemistry, University of Copenhagen, Universitetsparken 5, 2100 København Ø, Denmark.
  • Müter D; Nano-Science Center, Department of Chemistry, University of Copenhagen, Universitetsparken 5, 2100 København Ø, Denmark.
  • Carlsson M; Center for Mathematical Sciences, Lund University, Box 118, 22100 Lund, Sweden.
  • Gulliksson M; School of Science and Technology, Örebro University, 701 82 Örebro, Sweden.
  • Stipp SLS; Nano-Science Center, Department of Chemistry, University of Copenhagen, Universitetsparken 5, 2100 København Ø, Denmark.
  • Sørensen HO; Nano-Science Center, Department of Chemistry, University of Copenhagen, Universitetsparken 5, 2100 København Ø, Denmark.
J Magn Reson ; 308: 106597, 2019 11.
Article in En | MEDLINE | ID: mdl-31546178
ABSTRACT
The interpretation of nuclear magnetic resonance (NMR) data is of interest in a number of fields. In Ögren (2014) local boundary conditions for random walk simulations of NMR relaxation in digital domains were presented. Here, we have applied those boundary conditions to large, three-dimensional (3D) porous media samples. We compared the random walk results with known solutions and then applied them to highly structured 3D domains, from images derived using synchrotron radiation CT scanning of North Sea chalk samples. As expected, there were systematic errors caused by digitalization of the pore surfaces so we quantified those errors, and by using linear local boundary conditions, we were able to significantly improve the output. We also present a technique for treating numerical data prior to input into the ESPRIT algorithm for retrieving Laplace components of time series from NMR data (commonly called T-inversion).
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: J Magn Reson Journal subject: DIAGNOSTICO POR IMAGEM Year: 2019 Document type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: J Magn Reson Journal subject: DIAGNOSTICO POR IMAGEM Year: 2019 Document type: Article