  Machine learning is becoming increasingly important in scientific and
technological progress, due to its ability to create models that describe
complex data and generalize well. The wealth of publicly-available seismic data
nowadays requires automated, fast, and reliable tools to carry out a multitude
of tasks, such as the detection of small, local earthquakes in areas
characterized by sparsity of receivers. A similar application of machine
learning, however, should be built on a large amount of labeled seismograms,
which is neither immediate to obtain nor to compile. In this study we present a
large dataset of seismograms recorded along the vertical, north, and east
components of 1487 broad-band or very broad-band receivers distributed
worldwide; this includes 629,095 3-component seismograms generated by 304,878
local earthquakes and labeled as EQ, and 615,847 ones labeled as noise (AN).
Application of machine learning to this dataset shows that a simple
Convolutional Neural Network of 67,939 parameters allows discriminating between
earthquakes and noise single-station recordings, even if applied in regions not
represented in the training set. Achieving an accuracy of 96.7, 95.3, and 93.2%
on training, validation, and test set, respectively, we prove that the large
variety of geological and tectonic settings covered by our data supports the
generalization capabilities of the algorithm, and makes it applicable to
real-time detection of local events. We make the database publicly available,
intending to provide the seismological and broader scientific community with a
benchmark for time-series to be used as a testing ground in signal processing.
