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Unsupervised machine learning reveals slab hydration variations from deep earthquake distributions beneath the northwest Pacific

Gilbert Mao 1 Thomas P Ferrand 2, 3, 4 Jiaqi Li 1 Brian Zhu 1 Ziyi Xi 1 Min Chen 1 
4 Géodynamique - UMR7327
BRGM - Bureau de Recherches Géologiques et Minières (BRGM), ISTO - Institut des Sciences de la Terre d'Orléans - UMR7327 : UMR7327, INSU - CNRS - Institut national des sciences de l'Univers, UO - Université d'Orléans : UMR7327
Abstract : Abstract 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 $$\sim$$ ~ 1 km.
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Submitted on : Wednesday, June 8, 2022 - 8:43:08 AM
Last modification on : Thursday, June 9, 2022 - 3:37:09 AM

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Gilbert Mao, Thomas P Ferrand, Jiaqi Li, Brian Zhu, Ziyi Xi, et al.. Unsupervised machine learning reveals slab hydration variations from deep earthquake distributions beneath the northwest Pacific. Communications Earth & Environment, Springer Nature, 2022, 3 (1), pp.56. ⟨10.1038/s43247-022-00377-x⟩. ⟨insu-03690222⟩

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