TY - JOUR
T1 - Fast removal of powerline harmonic noise from surface NMR datasets using a projection-based approach on graphical processing units
AU - Kjaer-Rasmussen, Anders
AU - Griffiths, Matthew P.
AU - Grombacher, Denys
AU - Larsen, Jakob Juul
N1 - Publisher Copyright:
IEEE
PY - 2022
Y1 - 2022
N2 - Surface nuclear magnetic resonance (NMR) measurements are notorious for their low signal-to-noise ratio (SNR). Powerlines are probably the most common source of noise and give the greatest contribution to noise levels. The noise from powerlines manifests itself as sinusoidal signals oscillating at the fundamental powerline frequency (50 Hz or 60 Hz) and at integer multiples of this frequency. Modelling and subtraction of the powerline noise has been demonstrated as a highly applicable method for improving SNR and is common practice today. However, the methods used to determine the parameters of the powerline noise are computationally expensive. Consequently, it is difficult to do real-time noise removal during acquisition of field data and therefore also difficult to do real-time quality inspection of data. Here, we demonstrate how the removal of powerline noise in surface NMR data can be significantly faster. We obtain this through two new developments. First, we apply a projection-based method to determine the powerline model, which is twice as fast as the commonly applied least-squares solution of a matrix equation. Second, we obtain a further 10 to 25 times speed-up by exploiting the high-performance parallel computations offered by graphical processing units (GPUs). We demonstrate the method on a noise-only field data set with an embedded synthetic NMR signal.
AB - Surface nuclear magnetic resonance (NMR) measurements are notorious for their low signal-to-noise ratio (SNR). Powerlines are probably the most common source of noise and give the greatest contribution to noise levels. The noise from powerlines manifests itself as sinusoidal signals oscillating at the fundamental powerline frequency (50 Hz or 60 Hz) and at integer multiples of this frequency. Modelling and subtraction of the powerline noise has been demonstrated as a highly applicable method for improving SNR and is common practice today. However, the methods used to determine the parameters of the powerline noise are computationally expensive. Consequently, it is difficult to do real-time noise removal during acquisition of field data and therefore also difficult to do real-time quality inspection of data. Here, we demonstrate how the removal of powerline noise in surface NMR data can be significantly faster. We obtain this through two new developments. First, we apply a projection-based method to determine the powerline model, which is twice as fast as the commonly applied least-squares solution of a matrix equation. Second, we obtain a further 10 to 25 times speed-up by exploiting the high-performance parallel computations offered by graphical processing units (GPUs). We demonstrate the method on a noise-only field data set with an embedded synthetic NMR signal.
KW - Computational modeling
KW - graphical processing units
KW - Harmonic analysis
KW - Mathematical models
KW - Nuclear magnetic resonance
KW - Numerical models
KW - Power system harmonics
KW - powerline noise
KW - signal processing
KW - Signal to noise ratio
KW - Surface NMR
UR - http://www.scopus.com/inward/record.url?scp=85119611329&partnerID=8YFLogxK
U2 - 10.1109/LGRS.2021.3118064
DO - 10.1109/LGRS.2021.3118064
M3 - Journal article
AN - SCOPUS:85119611329
SN - 1545-598X
VL - 19
JO - IEEE Geoscience and Remote Sensing Letters
JF - IEEE Geoscience and Remote Sensing Letters
M1 - 8024005
ER -