Projects per year
Abstract
The most common noise in geophysical data is probably interference from powerlines. This noise manifests itself as a sinusoidal signal oscillating at the fundamental 50 or 60 Hz frequency of the power grid and as harmonic components oscillating at integer multiples. Many different mitigation strategies, tailored for the specific geophysical method, have been developed to target powerline noise. One method that applies to fully sampled data is model-based subtraction, where a model of the powerline noise is fit to the noisy dataset and subsequently subtracted. In most cases, this leads to significant improvements in the signal-to-noise ratio. However, the determination of the powerline model parameters, in particular the fundamental powerline frequency, is computationally expensive, as it requires repeated solutions of a least-squares problem. We demonstrate that the powerline frequency can be directly predicted with a scientific machine-learning-based approach. We work on both time domain-induced polarization and surface nuclear magnetic resonance data. We use a different network for each method to trade-off prediction accuracy and prediction speed. In both cases, the prediction accuracy is fully on par with standard methods, and we obtain speed-ups by factors of 400 and 10 for the two types of data.
Original language | English |
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Article number | 5923410 |
Journal | IEEE Transactions on Geoscience and Remote Sensing |
Volume | 60 |
Number of pages | 10 |
ISSN | 0196-2892 |
DOIs | |
Publication status | Published - 2022 |
Keywords
- Frequency prediction
- geophysical datasets
- powerline noise
- scientific machine learning
- surface nuclear magnetic resonance (NMR)
- time domain-induced polarization (TDIP)
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- 2 Finished
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Flood and Drought – Tracking water in the shallow subsurface
01/10/2019 → 30/09/2023
Project: Research
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GIRem: Guided Injection Remediation (GIRem )
Christiansen, A. V. (PI), Larsen, J. J. (PI), Maurya, P. K. (Participant), Liu, L. (Participant), Domenzain Gonzalez, D. (Participant), Kühl, A. K. (Participant), Meldgaard Madsen, L. (Participant), Barfod, A. (Participant), Nielsen, A. N. (Participant), Auken, E. (Participant) & Dalsgaard, K. E. (Participant)
01/07/2018 → 31/12/2022
Project: Research