Extracting and Characterizing Near-Fault Effects from Strong Motion Observations of the 2022 Chishang Earthquake Sequence
Abstract
Accurately characterizing near-fault ground motions, particularly permanent tectonic displacements and velocity pulses associated with rupture directivity, is essential for seismic hazard assessment and infrastructure design. However, conventional ground-motion processing can distort or remove these characteristics. This study extends existing baseline correction methods by synchronously processing all three components of strong-motion recordings to preserve near-fault effects. The proposed methodology is applied to 59 recordings from the 2022 M 6.5 Guanshan foreshock and M 6.9 Chishang mainshock in Taiwan. Comparison with nearby Global Navigation Satellite System (GNSS) observations demonstrates that the method reliably captures the amplitude and orientation of permanent tectonic displacements. Analysis of the processed recordings reveals pronounced velocity pulses and permanent displacements, with the largest peak-to-peak velocities concentrated along the direction of rupture propagation. A classification framework is also introduced to distinguish fling-step pulses from dynamic velocity pulses. The results indicate that fling-step pulses typically occur early in the ground shaking, whereas dynamic pulses span a broader temporal range. Furthermore, the spatial distribution and orientation of near-fault effects are consistent with bridge damage documented during post-earthquake reconnaissance. The resulting dataset provides ground-motion time histories that preserve near-fault characteristics for seismic hazard assessment and engineering applications.
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BibTeX
@misc{lavrentiadis2026extracting,
title = {{Extracting and Characterizing Near-Fault Effects from Strong Motion Observations of the 2022 Chishang Earthquake Sequence}},
author = {Grigorios Lavrentiadis and Domniki Asimaki and Trevor J. Carey and Henry Benjamin Mason},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.04120},
eprint = {2611.04120},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.04120},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Extracting and Characterizing Near-Fault Effects from Strong Motion Observations of the 2022 Chishang Earthquake Sequence
AU - Grigorios Lavrentiadis
AU - Domniki Asimaki
AU - Trevor J. Carey
AU - Henry Benjamin Mason
PY - 2026
DA - 2026/11/03
PB - arXiv
DO - 10.48550/arxiv.2611.04120
UR - https://arxiv.org/abs/2611.04120
AB - Accurately characterizing near-fault ground motions, particularly permanent tectonic displacements and velocity pulses associated with rupture directivity, is essential for seismic hazard assessment and infrastructure design. However, conventional ground-motion processing can distort or remove these characteristics. This study extends existing baseline correction methods by synchronously processing all three components of strong-motion recordings to preserve near-fault effects. The proposed methodology is applied to 59 recordings from the 2022 M 6.5 Guanshan foreshock and M 6.9 Chishang mainshock in Taiwan. Comparison with nearby Global Navigation Satellite System (GNSS) observations demonstrates that the method reliably captures the amplitude and orientation of permanent tectonic displacements. Analysis of the processed recordings reveals pronounced velocity pulses and permanent displacements, with the largest peak-to-peak velocities concentrated along the direction of rupture propagation. A classification framework is also introduced to distinguish fling-step pulses from dynamic velocity pulses. The results indicate that fling-step pulses typically occur early in the ground shaking, whereas dynamic pulses span a broader temporal range. Furthermore, the spatial distribution and orientation of near-fault effects are consistent with bridge damage documented during post-earthquake reconnaissance. The resulting dataset provides ground-motion time histories that preserve near-fault characteristics for seismic hazard assessment and engineering applications.
ER -