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Abstract

Summary

Machine Learning models, which were trained to solve generic problems can be reused on unseen datasets. Applying such models is easy and valuable results can often be obtained much faster than through alternative workflows involving reprocessing and expert knowledge. Trained models therefore have potential to save time and money in operational settings by changing the way we work. Here, we discuss which problems are suitable, which type of models are available and how models can be added to a library of shared models. We will show examples of seismic and well log models that are applied to blind test data sets. These models are released in a library in the cloud that is accessible to users of OpendTect Machine Learning software.

© EAGE Publications BV
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/content/papers/10.3997/2214-4609.202332013
2023-03-20
2024-04-27

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References

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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.202332013
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A Geophysics Library of Trained Machine Learning Models
Conference Proceedings 2023, 1 (2023); https://doi.org/10.3997/2214-4609.202332013
/content/papers/10.3997/2214-4609.202332013
/content/papers/10.3997/2214-4609.202332013
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