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 & 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.