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Improving the quality control of seismic data through active learning. (arXiv:2201.06616v2 [stat.ML] UPDATED)
Jan. 21, 2022, 2:11 a.m. | Mathieu Chambefort, Raphaël Butez, Emilie Chautru, Stephan Clémençon
cs.LG updates on arXiv.org arxiv.org
In image denoising problems, the increasing density of available images makes
an exhaustive visual inspection impossible and therefore automated methods
based on machine-learning must be deployed for this purpose. This is
particulary the case in seismic signal processing. Engineers/geophysicists have
to deal with millions of seismic time series. Finding the sub-surface
properties useful for the oil industry may take up to a year and is very costly
in terms of computing/human resources. In particular, the data must go through
different …
More from arxiv.org / cs.LG updates on arXiv.org
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