<< Applying deep learning to seismic data has revealed tremor and slip occur at all times—before and after known large-scale slow-slip earthquakes—rather than intermittently in discrete bursts, as previously believed. Even more surprisingly, the machine learning generalizes to other tectonic environments, including the San Andreas Fault. >>
Machine learning reveals earth tremor and slip occur continuously, not intermittently. Los Alamos National Laboratory. Feb 27, 2020.
https://m.phys.org/news/2020-02-machine-reveals-earth-tremor-intermittently.html
<< Slow earthquakes cyclically load fault zones and have been observed preceding major earthquakes on continental faults as well as subduction zones. Slow earthquakes and associated tremor are common to most subduction zones, taking place downdip from the neighboring locked zone where megathrust earthquakes occur. In the clearest cases, tremor is observed in discrete bursts that are identified from multiple seismic stations. By training a convolutional neural network to recognize known tremor on a single station in Cascadia, we detect weak tremor preceding and following known larger slow earthquakes, the detection rate of these weak tremors approximates the slow slip rate at all times, and the same model is able to recognize tremor from different tectonic environments with no further training. >>
Bertrand Rouet-Leduc, Claudia Hulbert, et al. Probing Slow Earthquakes With Deep Learning. Geophysical Research Letters. Volume 47, Issue 4. doi: 10.1029/2019GL085870. Jan 23, 2020.
https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2019GL085870
