3.7.1.2 Unsupervised Neural Networks

In the unsupervised approach, you want the network to come up with a "natural" division of the seismic data. This approach is very useful when you want to perform, for example, horizon-based or volume-based segmentation. After training the network and application of the neural network output to an element, the results should be interpreted.

The (single) pickset holds the example positions at which the software calculates the chosen attributes. Therefore, each position in the pickset will yield a vector of values. The result of the extraction of the attributes at each picked location is the training set.

The neural network tries to cluster this set of vectors. Similar vectors go into the same Segment. This operation can be seen as subdividing the hyperspace of the attribute vectors in compartments. Each compartment has a centre: the cluster center.

After the training, the network can be applied to a horizon, time-slice, or volume. That means that the vectors are extracted in a volume or along a horizon. The network can then classify all those vectors into a Segment. A vector can be close to the cluster centre or further away from it, which is indicated by the Match. The closer to the cluster center, the higher the match.