Interpreting borehole data with machine learning: A pilot rtudy

Robert Buxton, Donal Krouse, Cecile Massiot & Mark J. F. Lawrence
Classifying and interpreting multi-sensor geophysical borehole (wireline) data is normally undertaken manually by a geologist and is extremely resource intensive, both in terms of skilled people and time. Machine learning techniques have been applied to wireline data overseas; however, those techniques are not necessarily directly applicable to New Zealand sedimentary rocks and little investigation has been done in geothermal settings. This pilot study aims to investigate machine learning algorithms and approaches to allow the automated...

Registration Year

  • 2021
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Resource Types

  • Text
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Affiliations

  • Callaghan Innovation
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  • GNS Science
    1