Earthquake catalogs are complex, high-dimensional objects and as Fig. We suggest that a fresh approach using more powerful techniques is warranted. The oldest of them, Omori’s Law, was developed based on felt reports without the benefit of instrumental measurements. The relationships cited above date from 127, 77, and 33 years ago. So it appears that there is value in applying these longstanding relationships to improved earthquake catalogs, but our opinion is that much more needs to be done. Just in the past few years, the time dependence of the b-value has been used to try to anticipate the likelihood of large earthquakes during an ongoing earthquake sequence 6 and the ETAS model has been improved to better anticipate future large events 7. These empirical laws continue to prove their utility. large earthquakes 4, and the Epidemic Type Aftershock Sequence (ETAS) model 5 in which earthquakes are treated as a self-exciting process governed by Omori’s law for their frequency of occurrence and Gutenberg–Richter statistics for their magnitude. ![]() These include Omori’s law 3 that describes the temporal decay of aftershock rate, the magnitude-frequency distribution, with the b-value describing the relative numbers of small vs. 1) and provide a higher-resolution picture of seismically active faults.Įmpirical seismological relationships have played a key role in the development of earthquake forecasting. How much more? These more complete catalogs typically feature at least a factor of ten more earthquakes (Fig. We will soon have a next generation of earthquake catalogs that contain much more information. Progress has been realized in research mode to analyze the details of seismicity well after the earthquakes being studied have occurred, and machine-learning techniques are poised to be implemented in operational mode for real-time monitoring. These are the essential ingredient for building complex supervised models. ![]() They have proven opportune targets for machine learning in seismology mainly due to the large, labeled data sets, which are often publicly available, and that were constructed through decades of dedicated work by skilled analysts. All of these tasks have seen rapid progress due to effective implementation of machine-learning approaches. The serial components of earthquake monitoring workflows include: detection, arrival time measurement, phase association, location, and characterization. The past 5 years have seen a rapidly accelerating effort in applying machine learning to seismological problems.
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