New plugin for both operational geo-scientists, experimental geo-scientists and research geo-scientists. Machine Learning links the OpendTect Pro environment to the research world of Python, TensorFlow, Keras, PyTorch & Scikit Learn. The plugin is the successor of our popular Neural Networks plugin, which has been fully integrated into the new plugin.

Machine Learning offers workflows for: seismic, wells, and seismic-to-wells applications.

Train on real data extracted from multiple surveys, or on synthetic data generated by SynthRock, OpendTect's stochastic simulator.

Includes trained models for off-the-shelf applications such as fault prediction by a U-Net.

All commercial plugins require OpendTect Pro.


Among others Machine Learning offers the following features:

  • Create input training data from multiple surveys.
  • Convolutional Neural Networks, Random Forest Algorithms, Support Vector Machines, ADaBoost, ...
  • Trained models for direct application.
  • Option to import your own trained models.
  • Training options: new, resume and transfer training.
  • Image-to-Image workflows (seismic faults, facies, horizons, ...).
  • Image-to-Point workflows (seismic facies, chimneys, salt, ...).
  • Log-log prediction workflows.
  • All supervised and unsupervised workflows supported in the original Neural Networks plugin.

In combination with SynthRock:

  • Create synthetic data sets for seismic rock-property prediction workflows.
Dragbar Machine Learning plugin