1. Figures Cluster World Oct. 2004 OpendTect article
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2. Figure 1. Open Source model.
Third Party
dGB-Group
Ownership, M&S Responsibility
License and M&S fees
Waived fees (dGB plugins)
Commercial Users
Academic Users
Commercial Plug-ins
Base
Free Plug-ins
Figure 1. Open Source model.

3. Figure 2. OpendTect impression.

4. Figure 3. Geometrically consistent tracking of horizons and faults.

5. Input Attributes: Meta-Attribute ANNO 1
Energy
Input Attributes:
Energy, Frequency, Cube Similarity Continuity, Dip Var., Azimuth Var., Absorption, Curvature, ..
Interpreter’s
Knowledge
Meta-Attribute
ANN
Figure 4. “Meta-attribute” concept. Multiple attributes and interpreter’s knowledge are combined by a neural network to give the optimal view of the object of interest. In this case a salt dome.

6. Object detection Inversion Filtering Pattern recognition Rock
Chimneys
& dGB plugins
Salt
… and turbidites, 4D bodies, ….
Faults
Object
detection
Rock
properties
Inversion
Before
After dip-steered median filter
Filtering
Facies / Channels
Pattern
recognition
Figure 5. Application examples of dip-steering and neural network plugins to OpendTect.

7. a b c
Figure 6. Generating TheChimneyCube®: a) Picked examples b) neural network performance graphs (RMS error vs. training cycles, percentage mis-classification and input attributes colored to indicate relative importance, with red being most important) and c) overlay of chimney “probability” on seismic data.