Distributed acoustic sensing (DAS) offers a cost-effective solution for acquiring dense seismic data. Conventional fiber-optic cables trenched along the surface provide excellent well sampled recordings of surface waves similar to horizontal geophones. Here we focus on near-surface refraction tomography using first arrivals. These arrivals appear weaker on trenched DAS cable due to predominantly horizontal directivity, but innovative processing techniques can efficiently address this issue. Super-virtual refraction interferometry (SVRI) can enhance the signal-to-noise ratio of seismic refraction energy without knowledge of the subsurface model. SVRI is based on seismic interferometry and allows creation of many more virtual shots and/or receivers. By stacking multiple virtual gathers that have the same travel-paths we increase the signal and attenuate the random noise. We demonstrate effectiveness of SVRI to enhance the refracted arrivals on DAS data from a Smart DAS pilot acquired in a desert environment and show improvements in resolution and in depth on the inverted tomograms.


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  1. Alshuhail, A., Aldawood, A., and Hanafy, S.
    [2012] Application of super-virtual seismic refraction interferometry to enhance first arrivals: A case study from Saudi Arabia. The Leading Edge, 31(1), 34–39.
    [Google Scholar]
  2. Bakulin, A. and Calvert, R.
    , 2004, Virtual Source: new method for imaging and 4D below complex overburden: 74th Annual International Meeting, SEG, Expanded Abstracts, 2477–2480.
    [Google Scholar]
  3. Bakulin, A., Golikov, P., Erickson, E., Silvestrov, I., Kim, Y.S., Smith, R., and M.Al-Ali
    [2018] Smart DAS uphole acquisition system for near surface characterization and imaging: 88th Annual International Meeting, SEG, Expanded Abstracts, 16–20.
    [Google Scholar]
  4. Bakulin, A., Golikov, P., Smith, R., Erickson, K., Silvestrov, I., and Al-Ali, M.
    [2017] Smart DAS upholes for simultaneous land near-surface characterization and subsurface imaging. The Leading Edge, 36(12), 1001–1008.
    [Google Scholar]
  5. Becker, M., Coleman, T., Ciervo, C., Cole, M., and Mondanos, M.
    [2017] Fluid pressure sensing with fiber-optic distributed acoustic sensing. The Leading Edge, 36(12), 1018–1023.
    [Google Scholar]
  6. Bharadwaj, P., Schuster, G. T., and Mallinson, I.
    [2011] Super-virtual refraction interferometry: Theory. SEG, Expanded Abstracts, 3809–3813.
  7. Daley, T. M., Freifeld, B. M., Ajo-Franklin, J., Dou, S., Pevzner, R., Shulakova, V., Kashikar, S., Miller, D. E., Goetz, J., Henninges, J., and Lueth, S.
    [2013] Field testing of fiber-optic distributed acoustic sensing (DAS) for subsurface seismic monitoring. The Leading Edge, 32(6), 699–706.
    [Google Scholar]
  8. Dong, S., Sheng, J., & Schuster, G. T.
    [2006] Theory and practice of refraction interferometry. SEG, Expanded Abstracts, 3021–3025.
    [Google Scholar]
  9. Hanafy, S. M., and Al-Hagan, O.
    [2012] Super-virtual refraction interferometry: an engineering field data example. Near Surface Geophysics, 10(5), 443–449.
    [Google Scholar]
  10. Schuster, G.
    [2009] Seismic Interferometry. Cambridge University Press.
    [Google Scholar]
  11. Wapenaar, K. and Fokkema, J.
    [2006] Green's function representations for seismic interferometry. Geophysics, 71(4), SI33–SI46.
    [Google Scholar]

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