Label Data Exponentially Faster to Train and Apply Machine Learning Models

The Enthought seismic deep learning model building toolkit allows you to quickly label data, collaboratively interpret with models developed with Enthought experts, and use software technology designed for managing AI systems, models and their outputs.
deep learning process

Deep Learning Model Building Process

You Know Seismic. We Know Python.

There are two major barriers today to achieving the potential of deep learning in seismic interpretation: fast, consistent labeling of the seismic data, and a deep learning toolkit that enables quick experimentation with models, while iterating and managing all the data associated with your model and results. Enthought has developed scientific software technology that solves both.

Geophysicists need to produce enough labeled data, fast enough, to train deep learning models that will have business impact, both in terms of interpretation quality and expert efficiency.

The seismic toolkit’s labeling tool enables fast, efficient creation of a significant amount of trained data, necessary for deep learning models to perform. As the labeling process iterates, a combination of ‘low cost, fast to train’ deep-learning models and proprietary post-processing methods enables the rate of label creation to accelerate.

The value comes through a geoscientist operator’s deep expertise in geophysics and subsurface understanding, collaborating with Enthought expertise in AI, machine learning, coding, and foundational geophysics knowledge.

Plug and Play Models Optimized for Limited Training Data

The figure above shows cross-sections through the F3 3D seismic volume with sequence labels predicted using the trained AI, overlaid in color. Deep learning was used to derive a horizon on top of the “Salt” label, detailing the complicated geology immediately bounding the intrusion of the salt. The AI is initially trained with the interpretation of 5 inlines (out of 650). An initial prediction is then made on an additional 5 lines; any errors are corrected, and the AI continues training on the updated lines. Horizons are then extracted automatically from the resulting 3D volume(s) of predicted label probability.

Revolutionize the way you Interpret Seismic Data

The figure above shows the corresponding Shannon-entropy volume, which is derived from the prediction probability. Hot colors indicate that we cannot be confident of assigning a single label at these locations. Naturally, at the sequence boundaries, there’s a transition, so these regions have higher entropy. There also seems to be a giant “bird” buried under that ground; or is just the salt?

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