Neural Networks

Neural Networks are used for recognizing patterns, for extracting seismic objects and for quantitatively predicting rock properties from seismic inversion products.

The Neural Networks plugin in OpendTect supports supervised- and unsupervised learning approaches for clustering data and for finding non-linear relationships.

For optimal results the Neural Network plugin should be combined with the Dip Steering plugin, which supports pre-processing filters and extraction of attributes along seismic reflectors.

"OpendTect provides state-of-the-art pattern recognition tools to complement your eyeball quantitatively."
--Leon Thomsen, Principal, Delta Geophysics, former Senior Advisor and Principal Geophysicist, BP, and former S.E.G. President

All commercial plugins require OpendTect Pro.


The Neural Networks plugin supports Multi-Layer-Perceptrons (MLP) and Unsupervised Vector Quantizers (UVQ) networks to perform the following tasks:

  • Create Object “Probability” Cubes such as Chimney Cube, Salt Cube, Fault Cube, …
  • Create maps, or volumes with supervised classification results e.g. revealing fluid content classes, lithology classes, seismic facies classes, …
  • Create seismic facies pattern maps through unsupervised waveform clustering
  • Create volumes with 3D clusters through unsupervised attribute clustering
  • Create rock property volumes such as porosity, Sw, lithology class, … from seismic inversion products such as AI, EEI, Angle Stacks, ...

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