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

Earth Sciences

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

G. L. Mao, T. P. Ferrand, et al.

Discover the enigmatic mechanisms of deep earthquakes with groundbreaking research from Gilbert L. Mao and colleagues. Utilizing unsupervised machine learning on the Japan Meteorological Agency catalog, this study uncovers critical insights into b-values and faulting transformations in the northwestern Pacific region.... show more
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 ~1 km.
Publisher
Communications Earth & Environment
Published On
Mar 10, 2022
Authors
Gilbert L. Mao, Thomas P. Ferrand, Jiaqi Li, Brian Zhu, Ziyi Xi, Min Chen
Tags
deep earthquakes
unsupervised machine learning
b-values
transformational faulting
metastable olivine wedge
Pacific region
Japan Meteorological Agency
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