Guoguang Wei


Last Name

Wei

First Name

Guoguang

ORCID

0000-0002-8942-5659

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Publications 1 - 3 of 3
  • The 2023 Mw 6.8 Morocco Earthquake: A Lower Crust Event Triggered by Mantle Upwelling?
    Item type: Journal Article
    Huang, Kai; Wei, Guoguang; Chen, Kejie; et al. (2024)
    Geophysical Research Letters
    A M6.8 earthquake struck the High Atlas Mountains in Morocco on 8 September 2023, ending a 63-year seismic silence. We herein attempt to clarify the seismogenic fault and explore the underlying mechanism for this seismic event based on multiple data sets. Utilizing probabilistic Bayesian inversion on interferometric radar data, we determine a seismogenic fault plane centered at a depth of 26 km, striking 251° and dipping 72°, closely aligned with the Tizi n’Test fault system. Given a hypocenter at the Moho depth, the joint inversion of radar and teleseismic data reveals that the rupture concentrates between depths of 12 and 36 km, offsetting the Mohorovičić discontinuity (Moho) at ∼32 km. Considering a strong link between magma activity and failure in lower crust, we propose that the triggering of the earthquake possibly was mantle upwelling that also supports the high topography.
  • Simultaneous magnitude and slip distribution characterization from high-rate GNSS using deep learning: case studies of the 2021 Mw 7.4 Maduo and 2023 Turkey doublet events
    Item type: Journal Article
    Cui, Wenfeng; Chen, Kejie; Wei, Guoguang; et al. (2024)
    Geophysical Journal International
    Rapid and accurate characterization of earthquake sources is crucial for mitigating seismic hazards. In this study, based on 18 000 scenario ruptures ranging from Mw 6.4 to Mw 8.3 and corresponding synthetic high-rate Global Navigation Satellite System (HR-GNSS) waveforms, we developed a multibranch neural network framework, the continental large earthquake agile response (CLEAR), to simultaneously determine the magnitude and slip distributions. We apply CLEAR to recent large strike-slip events, including the 2021 Mw 7.4 Maduo earthquake and the 2023 Mw 7.8 and Mw 7.6 Turkey doublet. The model generally estimates the magnitudes successfully at 32 s with errors of less than 0.15, and predicts the slip distributions acceptably at 64 s, requiring only approximately 30 ms on a single CPU (Central Processing Unit). With optimal azimuthal coverage of stations, the system is relatively robust to the number of stations and the time length of the received data.
  • Detecting slow slip events in the Cascadia subduction zone from GNSS time series using deep learning
    Item type: Journal Article
    Wang, Ji; Chen, Kejie; Zhu, Hai; et al. (2024)
    GPS Solutions
    Slow Slip Events (SSEs) are like long-duration slow earthquakes during which stress is gradually released over several days to months, and a comprehensive catalog of SSEs is essential for a better understanding of the earthquake cycle. However, SSEs usually only produce mm to cm surface deformations, making them a challenge to identify from raw Global Navigation Satellite System (GNSS) time series, which are often obscured by low-frequency background noise. We devise an approach that first employs variational Bayesian Independent Component Analysis to improve the signal-to-noise ratio of GNSS time series and then utilizes deep learning combining bidirectional Long Short-Term Memory and two different attention mechanisms to identify SSEs. We apply this new method to the GNSS three-component time series at 240 stations along the Cascadia subduction zone from 2012 to 2022. A total of 56 SSEs are detected, 18 more than the number in the existing SSEs catalogs during the same period. The starting time, duration, spatial and propagation pattern of the 56 SSEs are consistent with the tremor catalog, which helps to gain new insights into the slip behavior in the Cascadia subduction zone. In general, our work provides an effective framework for extracting subtle signals hidden in GNSS time series.
Publications 1 - 3 of 3