Fast structural interpretation with structure-oriented filtering

By G. Fehmers, C. Höcker. 2002

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Sparsity- and continuity-promoting seismic image recovery with curvelet frames (2008, neutral): “, [24]) is given by Jc(m) = ∥∥ 1/2∇m∥∥22, (28) with ∇ the discretized gradient matrix defined as ∇ = [D1 DT2 ]T .”
Horizon volumes with interpreted constraints (2014, methodology): “In Figure 2, using only one control point to indicate which horizon we want to extract, our method updates the initially horizontal surface to the more nearly correct seismic horizon (blue curves in Figure 2) after nine iterations.”
Noise reduction by vector median filtering (2013, methodology): “Using structure tensors (Fehmers and Hocker, 2003; Luo et al., 2006), we computed the local dip vectors of seismic events at every sample location.”
Directional structure-tensor-based coherence to detect seismic faults and channels (2017, methodology): “…and Schunck, 1991; Van Vliet and Verbeek, 1995; Weickert, 1997), have been widely applied to seismic images in estimating the orientations of seismic structural and stratigraphic features and computing the isotropy or anisotropy of the features (Bakker, 2002; Fehmers and Höcker, 2003; Hale, 2009).”
Image-guided inversion of electrical resistivity data (2014, methodology): “The eigenvalue and eigenvector decomposition of T(x) is given by (Fehmers & Höcker 2003; Hale 2009a),”