Imaging subsurface structures in the San Jacinto fault zone with high-frequency noise recorded by dense linear arrays - Archive ouverte HAL
Journal Articles Geophysical Journal International Year : 2019

Imaging subsurface structures in the San Jacinto fault zone with high-frequency noise recorded by dense linear arrays

Abstract

S U M M A R Y Cross-correlations of 2-35 Hz ambient seismic noise recorded by three linear arrays across the San Jacinto Fault Zone (SJFZ) in Southern California are used to derive high-resolution shear wave velocity models for the top 50-90 m of the crust at the array locations. Coherent Rayleigh surface waves are inverted to construct 2-D maps of group velocities in the range 0.2-0.6 km s −1. These maps are inverted to shear wave velocities around the fault using a Markov Chain Monte Carlo approach. The results show marked low-velocity zones in the top 20-30 m with velocity reduction up to 35 per cent and shallow flower structures at depth shallower than 50 m. The derived velocities, location of low-velocity zone with respect to main surface traces and shape with depth are generally consistent with borehole measurements and previous imaging of deeper sections of the SJFZ at the same sites or nearby. The imaging technique requires only ∼30 d of data (90 per cent of the signal-to-noise ratio is obtained in 15 d) and it bridges an observational gap between surface geology and typical tomography studies with no resolution in the top 100 m.
Fichier principal
Vignette du fichier
Zigone_etal_GJI_2019.pdf (9.21 Mo) Télécharger le fichier
Origin Files produced by the author(s)
Loading...

Dates and versions

hal-02482587 , version 1 (18-02-2020)

Identifiers

Cite

Dimitri Zigone, Yehuda Ben-Zion, Maximilien Lehujeur, Michel Campillo, Gregor Hillers, et al.. Imaging subsurface structures in the San Jacinto fault zone with high-frequency noise recorded by dense linear arrays. Geophysical Journal International, 2019, 217 (2), pp.879 - 893. ⟨10.1093/gji/ggz069⟩. ⟨hal-02482587⟩
58 View
71 Download

Altmetric

Share

More