  Parameter values for seismic processing steps are often chosen on a regular
grid of samples and interpolated. Active learning instead attempts to optimally
select the samples on which parameter values are chosen. For parameters that do
not vary smoothly, this often reduces the number of samples that need to be
labelled in order to achieve a desired accuracy on the whole dataset. In
regression tasks this is typically achieved using a query by committee strategy
that selects the samples on which a committee of models is most uncertain. I
implement such a strategy for the first break picking task, where the
parameters to be chosen are the centre and width of the picking window for each
trace. For the committee members I use the centre of the picking window and
three popular picking algorithms. Applying this to a real dataset, and with
samples corresponding to shot gathers, the active learning approach primarily
selects gathers near a jump in the first breaks, and achieves similar levels of
accuracy on the whole dataset with about half the number samples picked as when
the samples are randomly selected.
