Probing Slow Earthquakes With Deep Learning
- Los Alamos National Laboratory, Geophysics Group Los Alamos NM USA
- Los Alamos National Laboratory, Geophysics Group Los Alamos NM USA, Laboratoire de Géologie, Département de Géosciences, École Normale Supérieure, PSL Research University, CNRS UMR Paris France
- Los Alamos National Laboratory, Geophysics Group Los Alamos NM USA, Department of Geophysics Stanford University Stanford CA USA
Slow earthquakes may trigger failure on neighboring locked faults that are stressed sufficiently to break, and slow slip patterns may evolve before a nearby great earthquake. However, even in the clearest cases such as Cascadia, slow earthquakes and associated tremor have only been observed in intermittent and discrete bursts. By training a convolutional neural network to detect known tremor on a single seismic station in Cascadia, we isolate and identify tremor and slip preceding and following known larger slow events. The deep neural network can be used for the detection of quasi‐continuous tremor, providing a proxy that quantifies the slow slip rate. Furthermore, the model trained in Cascadia recognizes tremor in other subduction zones and also along the San Andreas Fault at Parkfield, suggesting a universality of waveform characteristics and source processes, as posited from experiments and theory.
- Research Organization:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE
- Grant/Contract Number:
- 89233218CNA000001
- OSTI ID:
- 1601684
- Alternate ID(s):
- OSTI ID: 1601686
OSTI ID: 1659195
- Report Number(s):
- LA-UR--19-27444; e2019GL085870
- Journal Information:
- Geophysical Research Letters, Journal Name: Geophysical Research Letters Journal Issue: 4 Vol. 47; ISSN 0094-8276
- Publisher:
- American Geophysical Union (AGU)Copyright Statement
- Country of Publication:
- United States
- Language:
- English