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Abstract

Summary

Using a machine learning systems, a set of seismic documents has been automatically indexed on 25 metadata. The hold-out methodology has been used to evaluate the accuracy of the models. Results and lessons learnt are discussed.

© EAGE Publications BV
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/content/papers/10.3997/2214-4609.201803012
2018-11-30
2024-04-18

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References

  1. Blinston, K., H.Blondelle
    , 2017, Machine learning systems open up access to large volumes of valuable information lying dormant in unstructured documents: The Leading Edge. March2017, p257–261.
     [Google Scholar]
  2. Juneja, A., J.Micaelli and J.Johnston
    , 2017, Method and system for extracting, verifying and cataloging technical information from unstructured documents: US patent 20170169103 A1, www.google.com/patents/US20170169103.
     [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201803012
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Machine Learning To Support Technical Document Indexing, How To Measure The Accuracy?
Conference Proceedings 2018, 1 (2018); https://doi.org/10.3997/2214-4609.201803012
/content/papers/10.3997/2214-4609.201803012
/content/papers/10.3997/2214-4609.201803012
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