Nonlinear structure‐enhancing filtering using plane‐wave prediction*

Abstract

A B S T R A C TAttenuation of random noise and enhancement of structural continuity can significantly improve the quality of seismic interpretation. We present a new technique, which aims at reducing random noise while protecting structural information. The technique is based on combining structure prediction with either similarity-mean filtering or lower-upper-middle filtering. We use structure prediction to form a structural prediction of seismic traces from neighbouring traces. We apply a non-linear similarity-mean filter or an lower-upper-middle filter to select best samples from different predictions. In comparison with other common filters, such as mean or median, the additional parameters of the non-linear filters allow us to better control the balance between eliminating random noise and protecting structural information. Numerical tests using synthetic and field data show the effectiveness of the proposed structure-enhancing filters. I N T R O D U C T I O NExtracting structural information is the most important goal of seismic interpretation. A number of approaches have been proposed to preserve and enhance structural information. Hoeber, Brandwood and Whitcombe (2006) applied nonlinear filters, such as median, trimmed mean and adaptive Gaussian, over planar surfaces parallel to the structural dip. Hale (2009) and Fehmers and Höcker (2003) applied structure-oriented filtering based on anisotropic diffusion. Fomel and Guitton (2006) suggested the method of planewave construction. AlBinHassan, Luo and Al-Faraj (2006) developed 3D edge-preserving smoothing operators to reduce noise without blurring sharp discontinuities. Traonmilin and Herrmann (2008) used a mix of finite-impulse-response (FIR) and infinite-impulse-response filters, followed by f -x *

Predictive coherency

“…To make a structural prediction, we follow the predictive-painting algorithm (Fomel, 2010;Liu et al, 2010). In general, predictive painting can be defined as follows:…”

“…In this paper, we propose an attribute which is capable of identifying local changes in information across the fault and protecting them. The new attribute uses structure prediction (Liu et al, 2010) to form the structural prediction of seismic traces from their neighbors. We call this attribute predictive coherency.…”

Regularized least-squares migration of simultaneous-source seismic data with adaptive singular spectrum analysis

“…This approach can also be implemented with structure-preserving constraints to improve the migration results (Wang and Sacchi 2009 ). The angle-domain common-image gathers need more computation and storage, so Xue et al ( 2015 ) employ structure-enhancing filtering (Liu et al 2010 ; Swindeman and Fomel 2015 ) as a shaping regularization operator for effectively removing noise. The structure-enhancing filter is also used as a preconditioning operator that updates the image only along prominent dips (Chen et al 2015 ; Dutta and Schuster 2015 ), but the success of this approach significantly depends on the estimated dips.…”

Random noise attenuation using local signal-and-noise orthogonalization

“…Local seismic attributes measure seismic signal characteristics neither instantaneously, at each signal point, nor globally, across a data window, but locally in the neighborhood of each point (Fomel, 2007a). One of the most useful local attributes is local similarity, which has found numerous successful applications in different areas of seismic data processing: multicomponent image registration (Fomel et al, 2005;Fomel, 2007a), time-lapse registration (Fomel and Jin, 2009;Zhang et al, 2013), timefrequency analysis (Liu et al, 2011b), structure-enhancing filtering (Liu et al, 2010), phase estimation (Fomel and van der Baan, 2014), etc. By using a weighted stacking of seismic data according to local similarity to the reference trace, a seismic image with an increased signal-to-noise ratio can be obtained (Liu et al, 2009(Liu et al, , 2011a.…”