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PreprintarXiv licencearXiv

The BRUIT-FM "open data" noise reduction challenge

  • Wayne C Crawford
  • Stephan Ker
  • Simon Rebeyrol
  • Laurent Duval
Open at sourcePDFDOICite

Abstract

The BRUIT-FM Challenge asks participants to reduce ''noise'' --- defined as any signal that masks the signal of interest --- on an open dataset of real and synthetic broadband seafloor seismology data. The dataset includes signals from earthquakes, ocean infragravity waves and seafloor currents. It also includes training datasets of synthetic data with reference solutions. Participants are asked to remove or separate these signals to obtain the best earthquake and seafloor compliance signals. The authors will make a first assessment of the submissions, based on noise reduction, signal deformation and ease of use. Participants are invited to a community workshop, in which they will validate the preliminary assessment and collaborate on a community paper describing the results, identifying promising pathways and proposing new directions. The best results will be added to a new version of the dataset, which will persist as a benchmark for future developments.

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Cite

BibTeXRIS
BibTeX
@misc{crawford2026bruitfm,
  title = {{The BRUIT-FM "open data" noise reduction challenge}},
  author = {Wayne C Crawford and Stephan Ker and Simon Rebeyrol and Laurent Duval},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2606.05941},
  eprint = {2606.05941},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2606.05941},
}
RIS
TY  - UNPB
TI  - The BRUIT-FM "open data" noise reduction challenge
AU  - Wayne C Crawford
AU  - Stephan Ker
AU  - Simon Rebeyrol
AU  - Laurent Duval
PY  - 2026
DA  - 2026/10/14
PB  - arXiv
DO  - 10.48550/arxiv.2606.05941
UR  - https://arxiv.org/abs/2606.05941
AB  - The BRUIT-FM Challenge asks participants to reduce ''noise'' --- defined as any signal that masks the signal of interest --- on an open dataset of real and synthetic broadband seafloor seismology data. The dataset includes signals from earthquakes, ocean infragravity waves and seafloor currents. It also includes training datasets of synthetic data with reference solutions. Participants are asked to remove or separate these signals to obtain the best earthquake and seafloor compliance signals. The authors will make a first assessment of the submissions, based on noise reduction, signal deformation and ease of use. Participants are invited to a community workshop, in which they will validate the preliminary assessment and collaborate on a community paper describing the results, identifying promising pathways and proposing new directions. The best results will be added to a new version of the dataset, which will persist as a benchmark for future developments.
ER  -