17:15 〜 19:15
[SSS07-P15] DAS monitoring by using dark fiber on the campus of UL Lafayette
キーワード：DAS, Seismology
Distributed Acoustic Sensing (DAS) utilizes Rayleigh scattering within fiber optical cable to provide strain measurements. By this way, DAS can turn any fiber optical cable into a series of detectors to measure ground vibration data. Previous applications worldwide have shown that recorded data can be used to measure the Seismic Ambient Noise due to trains, traffic, and other sources [1]. We have conducted one month Distributed acoustic sensing (DAS) survey from June 26th to July 25th with a dark fiber cable at UL Lafayette campus to monitor environmental variation and urban activities by using the iDAS equipment from Silixa LLC [2]. The survey uses the 1 ms time sample rate and 1 m spatial sample rate. The raw cable length is about 2000 meters with coils at several junctions. After redundant cable signals at junctions are removed, the valid data length is about 1100 meters. The recorded data has been analyzed, showing events, such as traffic and lightening.
Interferometry is an established technology generating new acoustic responses of virtual sources by cross-correlating seismic observations at different receiver locations, which has been applied in our DAS to derive time-lapse surface data. The technique has been successfully used to transform traffic noise in the 2–30 Hz band (e.g. surface wave or Rayleigh waves generated by cars, trucks, and trains) into accurate and stable 1-D estimates of shear wave velocity from 0–30 m depth using DAS. We have applied the interferometry technology by cross-correlation and stacking 100 pieces data of 30 seconds duration with corresponding FK filtered to suppress the noise, as shown in the attached Figure a and Figure b shows the dispersion spectrum of the surface wave with dispersive curve picked, which can be used to derive dispersion curve.
Interferometry is an established technology generating new acoustic responses of virtual sources by cross-correlating seismic observations at different receiver locations, which has been applied in our DAS to derive time-lapse surface data. The technique has been successfully used to transform traffic noise in the 2–30 Hz band (e.g. surface wave or Rayleigh waves generated by cars, trucks, and trains) into accurate and stable 1-D estimates of shear wave velocity from 0–30 m depth using DAS. We have applied the interferometry technology by cross-correlation and stacking 100 pieces data of 30 seconds duration with corresponding FK filtered to suppress the noise, as shown in the attached Figure a and Figure b shows the dispersion spectrum of the surface wave with dispersive curve picked, which can be used to derive dispersion curve.