Data from: RockNet: Rockfall and earthquake detection and association via multitask learning and transfer learning

Seismological data can provide timely information for slope failure hazard
assessments, among which rockfall waveform identification is challenging
for its high waveform variations across different events and stations. A
rockfall waveform does not have typical body waves as earthquakes do, so
researchers have made enormous efforts to explore characteristic function
parameters for automatic rockfall waveform detection. With recent advances
in deep learning, algorithms can learn to automatically map the input data
to target functions. We develop RockNet via multitask and transfer
learning; the network consists of a single-station detection model and an
association model. The former discriminates rockfall and earthquake
waveforms. The latter determines the local occurrences of rockfall and
earthquake events by assembling the single-station detection model
representations with multiple station recordings. RockNet achieves macro
F1 scores of 0.990 and 0.981 in terms of discriminating earthquakes and
rockfalls from other events with the single-station detection and
association models, respectively.