Structure-oriented smoothing and semblance

By Dave Hale. 2009

110 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b428cf77bd3f03c994d94b573b8269156dd4c0c4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Jieyi Zhou
2. André Revil
3. Xinming Wu
4. Marios Karaoulis
5. Luming Liang
6. Yunzhi Shi
7. Dave Hale
8. Sergey Fomel

Citations in the corpus

• Image-guided inversion of electrical resistivity data (2014, methodology): “In our case, we approximate the image gradients by Gaussian derivatives, and the products of the components of those gradients are smoothed by Gaussian filters (see Deriche 1992; van Vliet et al. 1998; Hale 2006, 2009a, for details).”
• Horizon volumes with interpreted constraints (2014, background): “This measure could be planarity (Hale, 2009) or fault likelihood (Hale, 2013).”
• FaultSeg3D: Using synthetic data sets to train an end-to-end convolutional neural network for 3D seismic fault segmentation (2019, methodology): “…detection results that are, respectively, computed by using the methods of C3 (Gersztenkorn and Marfurt, 1999), C2 (Marfurt et al., 1999), planarity (Hale, 2009), structure-oriented linearity (Wu, 2017), structure-oriented semblance (Hale, 2009), fault likelihood (Hale, 2013; Wu and Hale, 2016),…”