  A similar waveform search plays a crucial role in seismology for detecting
seismic events, such as small earthquakes and low-frequency events. However,
the high computational costs associated with waveform cross-correlation
calculations represent bottlenecks during the analysis of long, continuous
records obtained from numerous stations. In this study, we developed a
deep-learning network to obtain 64-bit hash codes containing information on
seismic waveforms. Using this network, we performed a similar waveform search
for ~35 million moving windows developed for the 30 min waveforms recorded
continuously at 10 MHz sampling rates using 16 acoustic emission transducers
during a laboratory hydraulic fracturing experiment. The sampling points of
each channel corresponded to those of the 5.8-year records obtained from
typical seismic observations at 100 Hz sampling rates. Of the 35 million
windows, we searched for windows with small average Hamming distances among the
hash codes of 16 channel waveforms against template hash codes of 6057 events
that were catalogued using conventional autoprocessing techniques. The
calculation of average Hamming distances is 1000 times faster than that of the
corresponding network correlation. This hashing-based template matching enabled
the detection of 23,462 additional events. We also demonstrated the feasibility
of the hashing-based autocorrelation analysis, where similar event pairs were
extracted without templates, by calculating the average Hamming distances for
all possible pairs of the ~35 million windows. This calculation required only
15.5 h under 120 thread parallelisation. This deep hashing approach
significantly reduced the required memory compared with locality-sensitive
hashing approaches based on random permutations, enabling similar waveform
searching on a large-scale dataset.
