This attribute set contains a number of samples from the seismic data volume above and below the sample position. The set of samples describes the seismic waveform, and can be used in horizon based unsupervised segmentations. The workflow is as follows: 1) create a set of random picks along the horizon (Pickset menu), 2) train a UVQ network on examples extracted at the random pick locations (use (part of) the waveform as input), and 3) apply the trained network to the horizon (horizon menu).
"UVQ" default attribute-set
A horizon may cover a number of geological (sedimentary) environments. Generally each geological environment will generate a specific seismic response. An unsupervised neural network learns to segment the different seismic responses into different classes. Operating in this way channels, sand bars, bright spots, and other geological bodies might be detected. Note that the default set should not be used for segmentation of volumes because the input changes dramatically when we modify the extraction time position. For segmentation of 3D bodies, you should use phase-independent attributes (e.g. energy, similarity etc). In Figure 8-10 an example of horizon based segmentation in 8 classes is shown.
The default set should be modified such that the sample rate of the attribute set corresponds with the sample rate of the data, and that the sampled window covers the seismic response of the level of interest. In the default attribute set the segmentation is based on the waveform; another approach would be to segment on basis of a number of attributes such as energy, frequency, etc.
Figure 8-10. Horizon based unsupervised segmentation
To quickly create, apply and display an UVQ network, use this link: Quick UVQ
