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SEISMOLOGY

Data mining for seismic slip

Nature Geoscience volume 12, pages 5–6 (2019)Cite this article

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Seismic data during the time interval between larger earthquakes could contain information about fault displacements and potential for future failure, suggest analyses of data from laboratory and real-world slow-slip earthquakes using machine-learning techniques.

Tectonic faults fail in a number of different ways, ranging from aseismic creep to episodic slow slip, often associated with discrete bursts of tremor, all the way through to the fast slip that typically characterizes devastating earthquakes. Writing in Nature Geoscience, Hulbert et al.1 and Rouet-Leduc et al. 2 present insights into the relationships between slow- and fast-slip earthquakes and their seismic signals, using a machine-learning-based analysis of laboratory and field data.

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Fig. 1: Old bridge supports in Vancouver, Canada, upgraded in preparation for a potentially catastrophic seismic event.

Roy LANGSTAFF / Alamy Stock Photo

References

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  1. Department of Earth and Space Sciences, University of Washington, Seattle, WA, USA

    Kenneth C. Creager

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  1. Kenneth C. Creager
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Correspondence to Kenneth C. Creager.

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Creager, K.C. Data mining for seismic slip. Nature Geosci 12, 5–6 (2019). https://doi.org/10.1038/s41561-018-0281-7

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