Mechanisms of deep earthquakes unraveled thanks to unsupervised machine learning
- 1Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA (jli@epss.ucla.edu)
- 2Institute of Geological Sciences, Freie Universität Berlin, 12249, Berlin, Germany
- 3Department of Earth, Planetary, and Space Sciences, University of California, Los Angeles, CA 90095, USA
Although transformational faulting in the rim of the metastable olivine wedge is hypothesized as a triggering mechanism of deep-focus earthquakes, there is no direct evidence of such rim. Variations of the b value – slope of the Gutenberg-Richter distribution – have been used to decipher triggering and rupture mechanisms of deep earthquakes. However, detection limits prevent full understanding of these mechanisms. Using the Japan Meteorological Agency catalog, we estimate b values of deep earthquakes in the northwestern Pacific Plate, clustered in four regions with unsupervised machine learning. The b-value analysis of Honshu and Izu deep seismicity reveals a kink at magnitude 3.7–3.8, where the b value abruptly changes from 1.4–1.7 to 0.6–0.7. The anomalously high b values for small earthquakes highlight enhanced transformational faulting, likely catalyzed by deep hydrous defects coinciding with the unstable rim of the metastable olivine wedge, the thickness of which we estimate at ∼1 km.
How to cite: Li, J., Mao, G., Ferrand, T., Zhu, B., Xi, Z., and Chen, M.: Mechanisms of deep earthquakes unraveled thanks to unsupervised machine learning, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-9809, https://doi.org/10.5194/egusphere-egu23-9809, 2023.
