Nonlinear beamforming for enhancing prestack seismic data with a challenging near surface or overburden
Andrey Bakulin, Ilya Silvestrov, Maxim Dmitriev, Dmitry Neklyudov, Maxim Protasov, Kirill Gadylshin, Vladimir Tcheverda and Victor Dolgov
Journal name: First Break
Issue: Vol 36, No 12, December 2018 pp. 121 - 126
Info: Article, PDF ( 9.8Mb )
Price: € 30
Modern land seismic data acquisition is moving from sparse grids of large source/receiver arrays to denser grids of smaller arrays or point-source, point-receiver systems. Large arrays were designed to attenuate ground-roll and backscattered noise and to increase overall signal-to-noise ratio (SNR). An example of a typical raw common-shot gather acquired using a legacy acquisition design with 72 geophones in a group and five vibrators per sweep is shown in Figure 1b. We can clearly see that the ground-roll noise with low apparent velocity was partially attenuated by field arrays, and reflection events with high apparent velocity are strong and can be reliably identified. Decreasing the size of field arrays during acquisition in arid environments leads to dramatic decrease in data SNR. An example of raw common-shot gather from a 2D line acquired using a single-sensor survey is shown in Figure 1a. In contrast to legacy data, the single-sensor data is dominated by noise caused by severe multiple scattering in complex near-surface layers and shows no apparent evidence of reflection signal. The sources and receivers were spaced at 10 m intervals in this recent 2D single-sensor survey. This sampling involves much denser acquisition compared to the conventional data using intervals of 30 m or more. Theoretically, high-density seismic acquisition better samples the entire wavefield (signal and noise) and is expected to result in improved imaging. Achieving this in practice with huge amounts of low SNR data proves to be very challenging. Conventional time processing tools such as surface-consistent scaling, deconvolution, static corrections, require reliable prestack signal in the data. Their application to modern seismic datasets acquired with small arrays often leads to unreliable results because the derived operators are based on noise and not on the expected signal. To extract the maximum value from dense high-channel acquisition, we need to enhance signal in the prestack data. Fortunately, densely sampled data gives us more flexibility than grouping geophones directly in the field.