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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.

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Playback language: English
Abstract
Deep earthquakes' mechanisms remain unclear. This study uses unsupervised machine learning on the Japan Meteorological Agency catalog to estimate b-values of deep earthquakes in the northwestern Pacific. A kink at magnitude 3.7-3.8, with b-values changing from 1.4-1.7 to 0.6-0.7, reveals enhanced transformational faulting in a ~1 km thick, highly hydrated rim of the metastable olivine wedge.
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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