Crustal deformation characteristics in the southwest segment of the Longmenshan structural belt before Lushan Mw6.6 earthquake and seismogenic structural model

Xu, Dongzhuo; Li, Shenghu; Ta, La; Luo, Shunlong; Wei, Zehong; Zhu, Weiqiang; Xing, Shu

doi:10.1007/s11600-021-00654-x

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…Xu et al, 2013). By analyzing the crustal deformation patterns before this event, D. Xu et al (2021) inferred that the blind fault is likely immature. We generated the along-strike profile of the maximum slip amplitudes at depth (Figure 13b), which indicates a symmetric slip profile.…”

Section: Implications For Fault Maturitymentioning

confidence: 99%

Bayesian Inversion of Finite‐Fault Earthquake Slip Model Using Geodetic Data, Solving for Non‐Planar Fault Geometry, Variable Slip, and Data Weighting

2023

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Evaluating seismic hazard and understanding earthquake preparation processes require precise fault slip models which characterize the fault geometry and slip distribution. A precise fault slip model can assist in probing the accumulation and release of stress as well as the frictional behavior on the fault (Caniven et al., 2017;Collettini et al., 2011;Manighetti, 2005). Over the past decades, the temporal and spatial resolutions of the geodetic techniques which measure ground deformation have been significantly advanced. These geodetic techniques include global navigation satellite systems (GNSS), interferometric synthetic aperture radar (InSAR), optical imagery,

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Section: Implications For Fault Maturitymentioning

confidence: 99%

Bayesian Inversion of Finite‐Fault Earthquake Slip Model Using Geodetic Data, Solving for Non‐Planar Fault Geometry, Variable Slip, and Data Weighting

2023

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Deformation Characteristics of the Central South Segment of LFZ After Wenchuan Earthquake with SBAS-InSAR

Huang,

Liu,

Cao

et al. 2023

J Indian Soc Remote Sens

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Pre-earthquake anomaly extraction from borehole strain data based on machine learning

Chi,

Li,

Han

et al. 2023

Sci Rep

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Borehole strain monitoring plays a critical role in earthquake precursor research. With the accumulation of observation data, traditional data processing methods struggle to handle the challenges of big data. This study proposes a segmented variational mode decomposition method and a GRU-LUBE deep learning network based on machine learning theory. The algorithm enhances data correlation during decomposition and effectively predicts borehole strain data changes. We extract pre-earthquake anomalies from four-component borehole strain data of the Guza station for two major earthquakes in Sichuan (Wenchuan and Lushan earthquakes), obtaining more comprehensive anomalies than previous studies. Statistical analysis reveals similar abnormal phenomena in the Guza station’s borehole strain data before both earthquakes, suggesting shared crustal stress accumulation and release patterns. These findings highlight the need for further research to improve earthquake prediction and preparedness through understanding underlying mechanisms.

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Crustal deformation characteristics in the southwest segment of the Longmenshan structural belt before Lushan Mw6.6 earthquake and seismogenic structural model

Cited by 3 publications

References 23 publications

Bayesian Inversion of Finite‐Fault Earthquake Slip Model Using Geodetic Data, Solving for Non‐Planar Fault Geometry, Variable Slip, and Data Weighting

Bayesian Inversion of Finite‐Fault Earthquake Slip Model Using Geodetic Data, Solving for Non‐Planar Fault Geometry, Variable Slip, and Data Weighting

Deformation Characteristics of the Central South Segment of LFZ After Wenchuan Earthquake with SBAS-InSAR

Pre-earthquake anomaly extraction from borehole strain data based on machine learning

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