Attributes for fluvial channel interpretation
Specific 3-D seismic attributes can better help image fluvial channels in the subsurface and aid in the characterization of fluvial processes and further depositional analyses. Fluvial channels and systems are just one type of landform and process involved in the broader topic of geomorphology. Furthermore, fluvial channel interpretation using seismic attributes is just one aspect of seismic geomorphology. Certain fluvial features such as sinuosity, channel width, channel and levee deposits, oxbow lakes, and general flow direction can be highlighted and identified using seismic attributes. Attributes useful for highlighting these features and the characterization of fluvial channels include, but are not limited to: coherence, amplitude, shaded relief, dip magnitude and dip azimuth, curvature, spectral decomposition, sweetness and SPICE. The most commonly used seismic attributes for fluvial channel interpretation include: coherence, curvature, and spectral decomposition.
Coherence is a continuity attribute that detects edges or discontinuities in seismic data along a horizon or time slice. It is computed by measuring the similarity of seismic waveforms in different directions.  Low coherency is delineated by seismic character discontinuity across boundaries . Channels will exhibit this low coherency across the seismic trace as seen in Figure 2. Therefore, it is extremely useful in interpreting channel edges and channel geomorphology. When co-rendered with seismic amplitude, channelized features become even more apparent. In Figure 1, the combined amplitude and coherence attribute better image the channel seen trending diagonally in the bottom left corner of the image.
Curvature is a seismic attribute that measures the degree of curvature of seismic reflectors. It is defined as the derivative of both dip and azimuth and is a helpful attribute that can detect even the subtlest of channel features. In seismic interpretation of fluvial channels, the thalweg  of the channel is defined by the most-negative curvature, whereas the edges of a channel are defined by the most-positive curvature. Figure 3 shows how curvature can help detect the edges and thalweg of fluvial channels. In addition, channels are highlighted in curvature if there is differential compaction to adjacent deposits: for example, a lithologic contrast of a mud filled channel will produce most-negative channel curvature, whereas a sand filled channel will produce most-positive curvature. This is because mud is highly compressible and will form a slight depression or sag within a channel that is surrounded by compactible sand. Likewise, a channel filled with incompressible sediments such as sand and surrounded by mud will form a bump in the channel. These features can be detected with both seismic amplitude and curvature attributes, and are therefore helpful in determining channel fill lithology as seen in Figure 4.
Spectral decomposition is a frequency attribute that converts time into the frequency domain by Fourier analysis. Bed thicknesses will cause differences in amplitude response at different frequencies, which helps highlight channels in seismic stratigraphic analysis. For example, a higher frequency will result in a shorter wavelength and detect a thinner channel or part of a channel, whereas a lower frequency will result in a larger wavelength and detect a thicker channel or part of a channel. Figure 4 shows a cross sectional view of a channel, where the thalweg of the channel is shown by the lower frequencies and the point bars are shown by the higher frequencies.
The basic workflow as shown in Figure 5 of the spectral decomposition method involves conducting frequency decomposition into three different frequency magnitudes ranging from high to low and assigning three different colors (red, blue, and green) to them (cyan, magenta, and yellow (CMY) is also commonly used). Once this has been done, the frequency magnitudes are blended together with the result showing the differences in both frequency and therefore thickness as color variation and intensity. An example of a frequency slice with combined RGB blended frequencies is shown in Figure 6.
- Babikir, Ismailalwali & Salim, Ahmed & Ghosh, Deva. 2018. Stratigraphic characterization of a fluvial reservoir using seismic attributes and spectral decomposition: An example from the Northern Malay Basin. Petroleum and Coal. 60. 943-950.
- Torrado, Lucia & Mann, Paul & Bhattacharya, Janok. 2014. Application of seismic attributes and spectral decomposition for reservoir characterization of a complex fluvial system: Case study of the Carbonera Formation, Llanos foreland basin, Colombia.
- Alsouki, M., Taifour, R. & Al Hamad, O. J Petrol Explor Prod Technol (2014) 4: 123. https://doi.org/10.1007/s13202-014-0110-5
- Sajid, Muhammad & Ghosh, Deva & Ibrahim, Nor Azhar & Viratno, Bernato. 2014. Seismic attributes add a new dimension to prospect evaluation and geomorphology offshore Malaysia. The Leading Edge. 33. 536-545.
- Suarez, Yoscel, Marfurt, Kurt, and Falk, Mark. 2008. Seismic attribute-assisted interpretation of channel geometries and infill lithology: A case study of Anadarko Basin Red Fork channels.
- Laughlin, Kenny, Garossino, Paul, and Partyka, Gred. 2003. Spectral decomposition for seismic stratigraphic patterns.
- McArdle, N.J. and Ackers, M.A., 2012. Understanding seismic-thin bed responses using frequency decomposition and RGB blending. First Break, v.30, 57-65
 Meandering Fluvial Reservoirs
 Deepwater Elements and Architecture
 Identifying Complex Fluvial Sandstone Reservoirs Using Core, Well Log, and 3D Seismic Data
 Lithofacies Maps
 Width and Thickness of Fluvial Channel Bodies and Valley Fills in the Geological Record