Slowness adaptive f-k filtering of prestack seismic data

Abstract

Frequency‐wavenumber velocity filtering is often applied to prestack seismic data for the attenuation of coherent noise. Although the process often gives excellent results, it can sometimes result in signal smoothing and distortion and poor attenuation of coherent noise. A slowness adaptive f-k filter reduces signal distortion and improves the attenuation characteristics of the filter. The technique uses a time‐ and space‐variant narrow reject‐band f-k filter. Optionally, coherent noise is compressed before application of the filter. The apparent slowness of coherent noise events is estimated using local t-x slant stacks weighted by coherence. A two‐dimensional (2-D) window is moved across the shot record, and at each point on the record slant stacks are taken through the central sample of the window. The slowness value that produces the maximum stack is assigned to the central sample of the window. In this way, an instantaneous slowness image of the shot record is produced. A one‐dimensional (1-D), high‐pass, finite‐duration impulse‐response (FIR) filter is applied in a spatially and temporally varying way across the record on the basis of the instantaneous slowness values. Before filter application, trace‐to‐trace static and amplitude effects are estimated and removed from the data. This results in compression of coherent noise and improved attenuation after filtering. The filtering process has been applied to low‐fold prestack dynamite data from the Surat Basin, Australia. The results indicate that the technique has good attenuation characteristics and produces minimal distortion of seismic signal. The process, however, is computationally expensive.

Window length selection for optimum slowness resolution of the local-slant-stack transform

“…We determine the instantaneous slowness of seismic waves in the LSST domain as the slowness value with a maximum degree of coherence (see, e.g., Milkereit, 1987;Duncan and Beresford, 1994). The most commonly used coherence estimators are the semblance and the normalized crosscorrelation (Neidell and Taner, 1971;Taner et al, 1979); but many other options based on eigendecompositions of the covariance matrix (Key and Smithson, 1990), phase stacks (Schimmel and Paulssen, 1997), or even 3D extensions like the coherence cube (Marfurt et al, 1998(Marfurt et al, , 1999 are available.…”

“…Widespread LSST applications are local-adaptive filters, e.g., the spatial-averaging filters on degree-of-polarization measures Gallart, 2003, 2004), or the adaptive f-k filters of Duncan and Beresford (1994); and instantaneous slowness measures (Milkereit, 1987;van der Baan and Paul, 2000;Hu and Stoffa, 2009). Hence, the LSST is found in leading-edge algorithms, such as CRS, to estimate the first-order-approximation parameters or stereotomography (Billette and Lambaré, 1998;Billette et al, 2003;Lambaré, 2008) to measure local slope in the event picking.…”

A data-dependent Fourier filter based on image segmentation for random seismic noise attenuation

“…After all blocks are processed, they are merged together to form the filtered result. In comparison with the slowness adaptive local F-K filters (Stone, 1987;Duncan and Beresford, 1994;Qin et al, 2012) that suppose that the seismic events are linear in one block, the proposed method only assumes that seismic signals are coherent, which is more easy to be satisfied by real seismic data. Therefore, the proposed method is more flexible for random seismic noise attenuation compared with these slowness adaptive local F-K filters, since the DDFFs obtained by our method are more compact, especially for complex geological area.…”

“…To prevent signal distortion, the Fourier filter should be designed carefully. By supposing that seismic events are locally linear, some slowness adaptive local F-K filters (Stone, 1987;Duncan and Beresford, 1994;Qin et al, 2012) were proposed to achieve good signal preservation.…”

“…The Fourier filters, e.g., the 2D F-K filter and its 3D extension, the FKx-Ky filter, are widely applied in seismic signal processing for coherent and random noise attenuation (Peardon and Bacon, 1992;Duncan and Beresford, 1994;Lu, 2001;Naghizadeh, 2012;Qin et al, 2012;Hu and Lu, 2014). The original seismic data are transformed into the Fourier domain by multi-dimensional Fourier transform (2D or 3D).…”