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Plug-in Details
IPLAB Kohonen1D/2D/3D Classification3D
By
IPLAB LLC
Previous
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14Days - Evaluation
Quantity:
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Result of Kohonen 3D mapping and classification
Seismic cube cross section with multivariate space mapping and classification with 8 × 8 × 8 (512 total) classes 3D Kohonen neural network. The result cube is RGB mixed from the 3D Kohonen neural network.
Result of 1D seismic cube classification
Upper image is seismic cube cross section. The lower image is the classification results cube cross section.
IP_Classification3D input tab
Example for the input tab of the plug-in with several cubes for classification. The 3D Kohonen neural network option was selected for classification with 8 × 8 × 8 classes (512 total). The calculation was performed inside a layer that was specified by the top and bottom surfaces.
Scheme for Kohonen multivariate space 2D and 3D mapping
Scheme for Kohonen neural network multivariate space 2D and 3D mapping and classification. The multivariate vector will be mapped to 2D (two-index) classes or 3D (three-index) classes.
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Plug-in Attributes
Platform:
Petrel
Lifecycle
Exploration
Domain:
Geophysics
|
Geology and Modelling
Challenges:
Unconventional Heavy Oil | Carbonate and Fractures | Unconventional Shale
ECCN:
Russia origin, EAR99
Version
2018 | 2019 | 2020
Supporting Documents
Installation Guide
2018
|
2019
|
2020
|
Release Notes
2018
|
2019
|
2020
|
User Manual
2018
|
2019
|
2020
|
Others
Technical Article
|
Overview
Provides seismic cube classification using Kohonen 1D, 2D, and 3D neural network. The plug-in can be used for unsupervised classification using a seismic cube waveform based on a Kohonen self-organizing map (SOM) 1D, 2D, or 3D neural network. The results are virtual seismic cubes ready for RGB mixing.
Specifications
The 1D Kohonen neural network will create one index class. The 2D Kohonen neural network will create two indexed classes and thus will require RGB mix visualization. The 3D Kohonen neural network will create three indexed classes and will require RGB mix visualization. During the learning and calculation stages, the plug-in will use parallel processing based on all available CPU cores. It helps calculation performance for the 3D Kohonen neural network and saves significant time.
Features
Kohonen 2D or 3D projection provides better showing of object positions in multidimensional space
• RGB blending of 2D or 3D neural network projections potentially permits more accurate geological facies interpretation
Additional Information
A defined set of surface properties are used for classification. Optionally, it can be used for seismic waveform classification based on seismic cubes (one or several) and with layer definition (top and bottom surfaces).
Associated Plug-ins
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