Estimating Rayleigh surface wave from ambient noise recorded by Distributed Acoustic Sensing (DAS) dark fiber array in the city

Rafał Czarny, Tieyuan Zhu

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

Abstract

We present the processing workflow of estimating stable, good-quality Rayleigh surface waves from ambient noise recorded by the Distributed Acoustic Sensing (DAS) dark fiber array inside the city. Our example concerns a 660-m long telecom fiber line as a part of the Penn State Fiber-Optic For Environment Sensing (FORESEE) array. We process a month of continuous data with the seismic interferometry method. We focus on traffic noise which dominates in urban areas. In comparison to a standard ambient noise interferometry strategy, we added frequency-wavenumber wavefield separation before cross-correlation. We analyze the quality of every virtual shot gathers (VSGs) retrieved along with the DAS profile. It tuned out that high quality VSGs are those with virtual source points located near the obstacle on the road (bumps, joints, manholes) and some intersections. Eventually, we stack selected 5 best quality VSGs for both positive and negative wavenumbers according to the offset. Multi-mode Rayleigh surface wave with the broadband response from few Hz up to 40 Hz gives us the ability to reconstruct the 1-D S-wave velocity model. The quality of the estimated wave is promising in terms of monitoring small velocity changes due to external impact, e.g., water table variations.

Original languageEnglish (US)
Pages (from-to)2133-2137
Number of pages5
JournalSEG Technical Program Expanded Abstracts
Volume2022-August
DOIs
StatePublished - Aug 15 2022
Event2nd International Meeting for Applied Geoscience and Energy, IMAGE 2022 - Houston, United States
Duration: Aug 28 2022Sep 1 2022

All Science Journal Classification (ASJC) codes

  • Geotechnical Engineering and Engineering Geology
  • Geophysics

Fingerprint

Dive into the research topics of 'Estimating Rayleigh surface wave from ambient noise recorded by Distributed Acoustic Sensing (DAS) dark fiber array in the city'. Together they form a unique fingerprint.

Cite this