The Impact of the Three-Dimensional Structure of a Subduction Zone on Time-dependent Crustal Deformation Measured by HR-GNSS

Oluwaseun Idowu Fadugba, V. J. Sahakian,Diego Melgar,Arthur R. Rodgers, R. Shimony

EarthArXiv (California Digital Library)(2023)

引用 0|浏览0
暂无评分
摘要
Accurately modeling time-dependent coseismic crustal deformation as observed on high-rate Global Navigation Satellite System (HR-GNSS) lends insight into earthquake source processes and improves local earthquake and tsunami early warning algorithms. Currently, time-dependent crustal deformation modeling relies most frequently on simplified 1D radially symmetric Earth models. However, for shallow subduction zone earthquakes, even low-frequency shaking is likely affected by the many strongly heterogeneous structures such as the subducting slab, mantle wedge, and the overlying crustal structure. We demonstrate that including 3D structure improves the estimation of key features of coseismic HR-GNSS time series, such as the peak ground displacement (PGD), the time to PGD (tPGD), static displacements (SD), and waveform cross-correlation values. We computed 1D and 3D synthetic, 0.25 Hz and 0.5 Hz waveforms at HR-GNSS stations for four M7.3+ earthquakes in Japan using MudPy and SW4, respectively. From these synthetics, we computed intensity-measure residuals between the synthetic and observed GNSS waveforms. Comparing 1D and 3D residuals, we observed that the 3D simulations show better fits to the PGD and SD in the observed waveforms than the 1D simulations for both 0.25 Hz and 0.5 Hz simulations. We find that the reduction in PGD residuals in the 3D simulations is a combined effect of both shallow and deep 3D structures; hence incorporating only the upper 30 km 3D structure will still improve the fit to the observed PGD values. Our results demonstrate that 3D simulations significantly improve models of GNSS waveform characteristics and will not only help understand the underlying processes, but also improve local tsunami warning.
更多
查看译文
关键词
subduction zone,three-dimensional,time-dependent,hr-gnss
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要