Purpose: Create a Fingerprint "probability" Cube, i.e. a cube that shows how similar each position is to the position(s) where you created the fingerprint. Theory: The fingerprint attribute has the same objective as the neural network object detection method (e.g. Chimney Cube, Fault Cube): To detect similar seismic responses as the target response (e.g. HC bearing). The advantage of the fingerprint is that you only need to give examples of the object class itself (one point is sufficient). You don't have to pick counter examples (non-objects) as is the case in the neural network workflow. A fingerprint is created from selected attributes at the given input location(s). The ouput is a "probability" cube with values ranging between 0 (completely dissimilar) to 1 (identical to the fingerprint response). Software: OpendTect
Workflow:
Create a New Picksets, e.g. to capture the response at a hydrocarbon anomaly.
Pick one or more examples of the object under study.
Open the attribute set window and create a new attribute set with attributes on which your fingerprint should be based. Use Evaluate attributes to select attributes that show the object most clearly. To create a fingerprint for hydrocarbons investigate: energy, frequencies, AVO attributes etc.
Add the Fingerprint attribute, select the Pickset file, add the attributes that were defined above and Calculate the parameters (this means the attributes at the picked locations are extracted (and averaged) to calculate the fingerprint.
Apply the Fingerprint attribute to the seismic data in batch: Processing - Create seismic output, or on-the-fly: right-click on the element in the tree (e.g. part of an inline).
Tips:
The Fingerprint attribute assigns equal weights to each input attribute (this is where the fingerprint loses from the (non-linear) neural network seismic object detection technique). Therefore, try to limit the number of input attributes and do not add attributes that have virtually similar information.