OpendTect dGB Plugins User Documentation V3.0

dGB Earth Sciences

Copyright © 2002-2007 by dGB Beheer B.V.

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Table of Contents
1. Introduction
2. Steering
2.1. Background
2.2. Create Steering Data
2.2.1. Import Steering Data
2.2.2. Calculate Steering Data
2.2.2.1. Description
2.2.2.2. Create steering cube window
2.2.3. Filter
2.2.3.1. Description
2.2.3.2. Filter steering cube window
2.3. Attributes with steering
2.3.1. Curvature
2.3.1.1. Mean curvature
2.3.1.2. Gaussian curvature
2.3.1.3. Maximum curvature
2.3.1.4. Minimum curvature
2.3.1.5. Most positive curvature
2.3.1.6. Most negative curvature
2.3.1.7. Shape index
2.3.1.8. Dip curvature
2.3.1.9. Strike curvature
2.3.1.10. Contour curvature
2.3.1.11. Curvedness
2.3.1.12. General Remark
2.3.2. Dip
2.3.2.1. Polar dip
2.3.2.2. Azimuth
2.3.2.3. Inline dip
2.3.2.4. Crossline dip
2.3.3. Dip angle
2.3.4. Position
2.3.5. Reference shift
2.3.6. Similarity
2.3.7. Volume statistics
2.4. Benchmark steering cube creation
2.4.1. Speed vs. algorithm and calculation cube size
2.4.2. Visual quality check
2.4.3. Crossline dip attribute
2.4.4. Filtering of the steering cubes
2.4.5. Steered similarity attribute
2.4.6. Choosing a steering algorithm
3. Sequence Stratigraphic Interpretation System
3.1. Introduction
3.2. Data Preparation
3.2.1. Horizon preparation
3.2.2. Filtering the steering cube
3.3. Chrono Stratigraphy
3.3.1. Create Chrono Stratigraphy
3.3.2. Display Chrono Stratigraphy
3.4. Wheeler Transform / Wheeler Scene
3.4.1. Wheeler Scene
3.4.2. Create Wheeler Cube
3.5. Interpretations
4. Neural networks
4.1. Introduction
4.1.1. Supervised neural networks
4.1.2. Unsupervised neural networks
4.2. Neural network management window
4.3. Neural network information
4.3.1. Supervised neural network information
4.3.2. Seismic viewer window
4.3.2.1. Display Parameters
4.4. Import GDI networks window
4.5. New from PickSets
4.6. New from Well Data
4.7. NN training window
4.7.1. Supervised training from pickset
4.7.2. Supervised training from well data
4.7.3. Unsupervised training
5. Applications
5.1. How to Make TheChimneyCube®
5.1.1. Workflow
5.1.2. Picking example locations
5.1.3. Neural network training
5.1.4. Evaluation and application of the trained neural network
5.2. The Median-dip-filter
5.2.1. Example results
5.2.2. Create the Median dip filter yourself
5.2.3. Note
6. Default attribute sets
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
7. References
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