Denoising and improving the quality of seismic data using combination of DBM filter and FX deconvolution

作者：Bagheri Majid^*; Riahi Mohammad Ali; Hashemi Hosein

来源：Arabian Journal of Geosciences, 2017, 10(19): 440.

DOI：10.1007/s12517-017-3224-5

• FX deconvolution
• DBM filter
• Random noise attenuation
• Seismic data
• Signal to noise ratio

Seismic data denoising, random noise attenuation (RNA) and spike-like noise suppression, is a main consideration for improving the quality of records. RNA could increase signal to noise ratio (S/N) to avoid misinterpretation of seismic data. In this research, a novel method is created by using the combination of frequency-offset deconvolution (FXD) and decision-based median (DBM) filter for RNA from seismic data. The method is applied in two main phases; FXD is focused to remove the Gaussian noise and DBMfilter is focused to attenuate the impulsive noise and spikes. To implement and verify the method, three types of data are used: two synthetic models (a model with linear events and a model with hyperbolic events) and an observed seismic section. The ability of the proposed method (FXD-DBM) in comparison of applying each in seismic RNA application is proven. The noise level is reduced obviously, and hence, the S/N of all examined seismic records is increased considerably after denoising by the combination of FX deconvolution and DBM filter. About the real seismic section, suppressing random noise and spikes show up improving the seismic reflector continuity and hence enhancing the interpretability of data. Moreover, some masked events by random noise are clarified in different parts of data after denoising using the planned method.

• 出版日期2017-10