Chapter 6. Default attribute sets
- Table of Contents
- 6.1. NN Chimney Cube
- 6.2. NN Fault Cube
- 6.3. NN Salt Cube
- 6.4. NN Slump Cube
- 6.5. Unsupervised segmentation 2D
- 6.6. dGB Evaluate Attributes
- 6.7. Ridge-enhancement filtering
The steering and neural networks plugins for OpendTect are provided with default attribute sets to get the user started. The sets have proven their value in several studies, and generally deliver good results in their respective applications. Attribute sets starting with NN are meant as input for a neural network and are optimized to detect certain geological features, The other attribute sets have different other purposes. Note that all default attribute sets need a steering cube.
In general default sets give satisfactory results and therefore inexperienced user can use the sets without modifications. Experienced users can use the sets as starting point for attribute analysis. Fine-tuning is done by modifying attribute parameters, and/or adding or removing attributes. To give the user a good idea about the applicability of the default attribute set typical examples are provided in the next sections of this appendix. All examples contain a short description of the attribute set and its characteristics, an example of seismic data and the result after applying the default attribute set to this data.
6.1. NN Chimney Cube
This attribute set is meant for usage in a neural network. A key feature of this set is that most attributes are extracted in three separate time windows: one above, one centered around and one below the point of investigation. In this way we utilize the fact that chimneys are vertical bodies with a certain dimension. It is expected that similar seismic characteristics of chimneys are present in all three windows and thus a correlation exists between these three windows. The neural network will recognize this and will thus be able to distinguish between real chimneys and other (more localized) features.
In this section we compare the original seismic in Figure 6-1 with the results of chimney detecting neural network displayed in Figure 6-2. The main body of the chimney and its sidetracks are picked up, while other features (faults, low similarity/low energy layers) are rejected. In addition we compare a similarity attribute in Figure 6-3 with the neural network results in Figure 6-2. The similarity attribute highlights the chimney, but also other (unwanted) features are enhanced. The multi-attribute neural network is able to make a clear distinction between chimney and non-chimney. Neural networks with the NN Chimney Cube attribute set as input can (and in general will) also detect other fluid migration paths, e.g. dewatering structures.
