Simultaneous sources

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Simultaneous sources acquisition is a new acquisition to reduce acquisition cost. With more than one sources or air guns fired simultaneously, acquisition time and cost can be reduced significantly. As the recorded dataset are blended together in simultaneous acquisition, a following deblending process is usually needed before any further denoise and analysis.

Deblending methods are different due to the differences of sources in land and marine acquisition system.

Land simultaneous acquisition

Land simultaneous acquisition are usually configured by several vibroseises, which are fired with signatures coded differently (orthogonally). The recorded blended dataset are correlated with each signature and yield the related clean deblending result.

Marine Simultaneous acquisition

For marine acquisition, it is not easy to "code" the signature for airguns. Thus, airguns are usually shooted at a random firing time, which makes the interfering shots becoming random spikes in common receiver, offset or CMP domain (as shown in Figure 1). Despiking methods such as median filter (Wang et al., 2012 [1]), space-varying median filter (SVMF) (Chen, 2015 [2]), structure-oriented median filter (Gan et al., 2016 [3]), and singular spectral analysis (Cheng and Sacchi, 2015 [4]) can be used for the deblending marine simultaneous data.

It has been shown by a lot of researchers that inversion based deblending methods are more powerful than the filtering based methods as mentioned above. Iterative inversion methods include shaping regularization based iterative seislet thresholding method (Chen et al., 2014 [5]), iterative curvelet thresholding method (Zu et al., 2016 [6]; Qu et al., 2016 [7]), iterative orthogonalization and seislet thresholding (Chen, 2015 [8]), iterative seislet frame thresholding method (Gan et al., 2016 [9]), iterative amplitude-preserving Radon thresholding (Xue et al., 2017 [10]), iterative rank-increasing method (Xue et al., 2017 [11]), etc.

Fig 1. blended records of random firing time in different domain (Hampson et al, 2008 [12]).

Direct imaging and inversion

The simultaneous source data can be either first-separated and second-processed, or directly used for imaging and inversion.

Least-squares reverse time migration (LSRTM) is currently the dominant way to directly migrate simultaneous-source data. Due to the strong migration artifacts, effective anti-noise regularization operator needs to applied, such as the smoothing filter constrained LSRTM (Chen et al., 2015 [13]; Xue et al, 2016 [14]), lowrank low-rank constrained edge-preserving LSRTM (Chen et al., 2017 [15]). However, one of the biggest problems in direct imaging is a fairly acceptable macro velocity model of subsurface structure, which requires sophisticated velocity analysis techniques (Gan et al., 2016 [16]).


References

  1. Huo, Shoudong, Yi Luo, and Panos G. Kelamis. "Simultaneous sources separation via multidirectional vector-median filtering." Geophysics 77.4 (2012): V123-V131.
  2. Chen, Y., 2015, Deblending using a space-varying median filter, Exploration Geophysics, 46, 332-341.
  3. Gan, S., S. Wang, Y. Chen, X. Chen, and Kui Xiang, 2016, Separation of simultaneous sources using a structural-oriented median filter in the flattened dimension, Computers & Geosciences, 86, 46-54.
  4. Cheng, Jinkun, and Mauricio D. Sacchi. "Separation and reconstruction of simultaneous source data via iterative rank reduction." Geophysics 80.4 (2015): V57-V66.
  5. Chen, Y., S. Fomel, and J, Hu, 2014, Iterative deblending of simultaneous-source seismic data using seislet-domain shaping regularization, Geophysics, 79, V183-V193.
  6. Zu, S., H. Zhou, Y. Chen, S. Qu, X. Zou, H. Chen, and R. Liu, 2016, A periodically varying code for improving the deblending of simultaneous sources in marine acquisition, Geophysics, 81, V213-V225.
  7. Qu, S., H. Zhou, R. Liu, Y. Chen, S. Zu, S. Yu, J. Yuan, and Y. Yang, 2016, Deblending of simultaneous-source seismic data using fast iterative shrinkage-thresholding algorithm with firm-thresholding: Acta Geophysica, 64, 1064-1092.
  8. Chen, Y., 2015, Iterative deblending with multiple constraints based on shaping regularization, IEEE Geoscience and Remote Sensing Letters, 12, 2247-2251.
  9. Gan, S., S. Wang, Y. Chen, and X. Chen, 2016, Simultaneous-source separation using iterative seislet-frame thresholding, IEEE Geoscience and Remote Sensing Letters, 13, 197-201.
  10. Xue, Y., M. Man, F. Chang, S. Zu, and Y. Chen, 2017, Amplitude-preserving iterative deblending of simultaneous source seismic data using high-order Radon transform, Journal of Applied Geophysics, 2017, 139, 79-90.
  11. Xue, Y., F. Chang, D. Zhang, and Y. Chen, 2016, Simultaneous sources separation via an iterative rank-increasing method, IEEE Geoscience and Remote Sensing Letters, 13, 1915-1919.
  12. Hampson, Gary, Joe Stefani, and Fred Herkenhoff. "Acquisition using simultaneous sources." The Leading Edge 27.7 (2008): 918-923.
  13. Chen, Y., J. Yuan, S. Zu, S. Qu, and S. Gan, 2015, Seismic imaging of simultaneous-source data by constrained least-squares reverse time migration, Journal of Applied Geophysics, 114, 32-35.
  14. Xue, Z., Y. Chen, S. Fomel, and J. Sun, 2016, Seismic imaging of incomplete data and simultaneous-source data using least-squares reverse time migration with shaping regularization, Geophysics, 81, S11-S20.
  15. Chen, Y., H. Chen, K. Xiang, and X. Chen, 2017, Preserving the discontinuities in least- squares reverse time migration of simultaneous-source data, Geophysics, 82, S185-S196.
  16. Gan, S., S. Wang, Y. Chen, S. Qu, and S. Zu, 2016, Velocity analysis of simultaneous-source data using high-resolution semblance - coping with the strong noise, Geophysical Journal International, 216, 768-779.