  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 steps of noise attenuation. Each denoise step is then ideally
followed by a quality control (QC) stage performed by means of human expertise.
To learn a quality control classifier in a supervised manner, labeled training
data must be available, but collecting the labels from human experts is
extremely time-consuming. We therefore propose a novel active learning
methodology to sequentially select the most relevant data, which are then given
back to a human expert for labeling. Beyond the application in geophysics, the
technique we promote in this paper, based on estimates of the local error and
its uncertainty, is generic. Its performance is supported by strong empirical
evidence, as illustrated by the numerical experiments presented in this
article, where it is compared to alternative active learning strategies both on
synthetic and real seismic datasets.
