Details zur Publikation |
| Kategorie | Textpublikation |
| Referenztyp | Zeitschriften |
| DOI | 10.1002/nsg.12324 |
Lizenz  |
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| Titel (primär) | Score filtering for contextualized noise suppression of Poisson distributed geophysical signals |
| Autor | Altdorff, D.; Schrön, M. |
| Quelle | Near Surface Geophysics |
| Erscheinungsjahr | 2024 |
| Department | CHS; MET |
| Band/Volume | 22 |
| Heft | 6 |
| Seite von | 599 |
| Seite bis | 616 |
| Sprache | englisch |
| Topic | T5 Future Landscapes |
| Daten-/Softwarelinks | https://doi.org/10.7910/DVN/9GDPNA |
| Keywords | CRNS; data smoothing; EMI; gamma spectrometry; noise suppression |
| Abstract | Geophysical and remote sensing products that rely on Poisson-distributed measurement signals, such as cosmic-ray neutron sensing (CRNS) and gamma spectrometry, often face challenges due to inherent Poisson noise. Common techniques to enhance signal stability include data aggregation or smoothing (e.g., moving averages and interpolation). However, these methods typically reduce the ability to resolve detailed temporal (stationary data) and spatial (mobile data) features. In this study, we introduced a method for contextual noise suppression tailored to Poisson-distributed data, utilizing a discrete score attribution system. This score filter evaluates each observation against eight different criteria to assess its consistency with surrounding values, assigning a score between 0 (very unlikely) and 8 (very likely) to indicate whether the observation is likely to act as noise. These scores can then be used to flag or remove data points based on user-defined thresholds. We tested the score filter's effectiveness on both stationary and mobile CRNS data, as well as on gamma-ray spectrometry and electromagnetic induction (EMI) recordings. In our examples, the score filter consistently outperformed established filters, for example Savitzky–Golay and Kalman, in direct competition when applied to CRNS time series data. Additionally, the score filter substantially reduced Poisson noise in mobile CRNS, gamma-ray spectrometry and EMI data. The scoring system also provides a context-sensitive evaluation of individual observations or aggregates, assessing their conformity within the dataset. Given its general applicability, customizable criteria and very low computational demands, the proposed filter is easy to implement and holds promise as a valuable tool for denoising geophysical data and applications in other fields. |
| dauerhafte UFZ-Verlinkung | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=29769 |
| Altdorff, D., Schrön, M. (2024): Score filtering for contextualized noise suppression of Poisson distributed geophysical signals Near Surf. Geophys. 22 (6), 599 - 616 10.1002/nsg.12324 |
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