Optimal deghosting robust to nonstationary noise from multimeasurement streamer data

Abstract

The optimal deghosting (ODG) method is used to combine pressure and vertical velocity in a way to optimally minimize the impact of noise on the deghosted data. This is done by estimating the noise power spectrum of the pressure and vertical gradient to weight the contribution of each measurement. In general, the pressure and vertical velocity noise spectrums are estimated from noisedominant data windows assuming that the noise is spacetime stationary in the wide sense. If the noise statistics change with time or space, then the ODG solution is not optimal with respect to the signal to noise ratio (S/N). We propose a robust way to minimize the impact of nonstationary noise in ODG without estimating the noise power.

Three‐dimensional receiver deghosting of seismic streamer data using L1 inversion and redundant extended radon dictionary

“…(); Ozbek et al . (); Kamil and Caprioli (); Kamil, Yadari and Vassallo (); and Poole () proposed to use multi‐component data for receiver deghosting. Wu et al .…”

“…Posthumus (1993) and Ferber, Caprioli and West (2013) used streamers at different depths to deghost. Soubaras (1996); Carlson et al (2007); Robertsson et al (2008); Ozbek et al (2010); Kamil and Caprioli (2014); Kamil, Yadari and Vassallo (2014); and Poole (2014) proposed to use multicomponent data for receiver deghosting. Wu et al (2014) and Wang, Ray and Nimsaila (2014) used a progressive sparse τ −p x -p y inversion method to perform 3D joint deghosting and crossline interpolation using single-component pressure data.…”