Detecting slow slip events in the Cascadia subduction zone from GNSS time series using deep learning

Wang，Ji1; Chen，Kejie1,2; Zhu，Hai1; Hu，Shunqiang3; Wei，Guoguang1,4; Cui，Wenfeng1; Xia，Lei1

2024-10-01

10.1007/s10291-024-01701-y

GPS Solutions   影响因子和分区

1080-5370

1521-1886

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.

Deep learning Global Navigation Satellite System Slow slip events Variational bayesian independent component analysis

SCI ; EI

英语

第一 ; 通讯

20243016738964

Communication satellites ; Deep learning ; Earthquakes ; Global positioning system ; Independent component analysis ; Remote sensing ; Signal processing ; Signal to noise ratio ; Time series analysis

Ergonomics and Human Factors Engineering:461.4 ; Seismology:484 ; Communication Satellites:655.2.1 ; Information Theory and Signal Processing:716.1 ; Mathematical Statistics:922.2

GEOSCIENCES

2-s2.0-85199099769

Scopus

1.Department of Earth and Space Sciences,Southern University of Science and Technology,Shenzhen,518055,China
2.Institute of Risk Analysis,Prediction and Management (Risks-X),Academy for Advanced Interdisciplinary Studies,Southern University of Science and Technology,Shenzhen,518055,China
3.Key Laboratory of Poyang Lake Wetland and Watershed Research,Ministry of Education,Jiangxi Normal University,Nanchang,330022,China
4.Institute of Geophysics,Department of Earth Sciences,ETH Zürich,Zurich,Switzerland

Wang，Ji,Chen，Kejie,Zhu，Hai,et al. Detecting slow slip events in the Cascadia subduction zone from GNSS time series using deep learning[J]. GPS Solutions,2024,28(4).

Wang，Ji.,Chen，Kejie.,Zhu，Hai.,Hu，Shunqiang.,Wei，Guoguang.,...&Xia，Lei.(2024).Detecting slow slip events in the Cascadia subduction zone from GNSS time series using deep learning.GPS Solutions,28(4).

Wang，Ji,et al."Detecting slow slip events in the Cascadia subduction zone from GNSS time series using deep learning".GPS Solutions 28.4(2024).