Diverse algorithms have been developed for efficient earthquake signal
processing and characterization. These algorithms are becoming
increasingly important as seismologists strive to extract as much
insight as possible from exponentially increasing volumes of continuous
seismic data. Deep neural networks have been shown to be promising tools
for this. We have developed a number of deep learning tools for more
efficient processing and characterizing of earthquake signals. In my
presentation, I demonstrate the performance of some of these tools
applied to seismic data. AI-based techniques have the potential to
improve our monitoring ability and as a result understanding of
earthquake processes and hazards.