Using Hilbert Transform as a Tool for Random Noise Attenuation and It’s Application from One of the Iranian South East Oilfields

Abstract

Using Hilbert transform as a tool for random noise attenuation and it’s application at one of the Iranian south east oilfields. Seismic data always consist of a signal and a noise component. What has to be considered as noise depends on the application? However, as a general defnition we can say that any recorded energy which interferes with the desired signal can be considered as noise. The noise can be classifed as background noise (for instance wind, swell, noise from nearby production, or interference from nearby seismic acquisition), source-generated noise (for instance direct and scattered waves or multiples), and instrument noise and can show up as coherent or incoherent energy in seismic gathers. This diversity of noise types with different characteristics makes separation of signal and noise a challenging process. In recent years, several authors have developed effective methods of eliminating random noise (incoherent energy). For example, Gulunay (2000) used the noncausal prediction filter for randomnoise attenuation; Ristau and Moon (2001) compared several adaptive filters, which they applied in an attempt to reduce random noise in geophysical data; and Karsli et al. (2006) applied complex-trace analysis to eismic data for random noise suppression, recommending it for low-fold seismic data. Also Some transform methods were used to eliminate seismic random noise, e.g., seislet transform (Fomel, 2006; Fomel and Liu, 2008), and curvelet transform (Neelamani et al., 2008). We use a procedure, complex-trace transformation (CTT) that uses complex-trace envelope and normalized phase traces to improve the temporal resolution and to increase S/N of stacked seismic data. We follow the technique offered by Shtivelman et al. (1986) and Karsli et al. (2006) who showed that analysis of seismic data in complex geologic environments should be based on both group and phase correlation. In this study, we suggest further applications of the method for resolution improvement and randomnoise suppression, which have not been considered by Shtivelman et al. (1986) and Karsli et al. (2006). The applicability of the method for temporal resolution improvement in presence of interference effects and random noise is also discussed with examples of synthetic and field seismic data from one of the Iranian south east oil fiels where the Hilbert transform denoising method has successfully attenuated the noise and allowed for improved imaging.