Automated Population and Validation of Geologic Logging Fields: An Approach to Autopopulate Select Logging Parameters and Rapidly Identify Mis-Logged Interval Candidates

Lynnette L. Hutson, Isabel Barton, Logan Hill, William Stavast, Seokjun Youn

Research output: Contribution to journalArticlepeer-review

Abstract

Geological characterization of drillholes is a crucial source of exploration data, but is a time-consuming, laborious, and manual process. This paper tests two ways that automation can help speed up drill core logging and assessing core log accuracy: (1) autopopulation of interpretive parameters from entered descriptions and (2) autovalidation, or automatic identification and flagging of potentially mis-logged intervals. These two approaches were tested using geologic logging data from 15 + years of drilling at the Morenci copper porphyry deposit. Autopopulation of an interpretive field (rock type) from the values entered in a descriptive field, including, e.g., grain size, color, texture, and mineralogy, with a Complementary Naïve Bayes classifier resulted in 89% accuracy. The autovalidation test applied two methods. The first used text mining to extract the rock type description from within a comment field and cross-referenced it to the manually logged rock code parameter, testing the rock code for inconsistency with the rock type description. The second used the Apriori algorithm to develop association rules ranking the commonness of mineral combinations in the same logging interval. The rarest mineral combinations may be erroneous and were automatically flagged for further review. Both autovalidation methods combined resulted in approximately 4300 logged intervals identified as potentially inaccurate. Eliminating typographical errors and similar minor problems narrowed this down to ~ 1000 potentially mis-logged intervals. Review by geologists confirmed that 350 of them were incorrectly logged. This study demonstrates the viability of using automation as a complement to the conventional manual core logging process, even in the absence of sophisticated automated logging systems. While the benefits of such approaches are incremental, they may still yield significant time savings for the operation given the volume of core and time and labor intensity of logging it.

Original languageEnglish (US)
Pages (from-to)3641-3658
Number of pages18
JournalMining, Metallurgy and Exploration
Volume41
Issue number6
DOIs
StatePublished - Dec 2024

Keywords

  • Apriori algorithm
  • Autopopulation
  • Autovalidation
  • Core logging
  • Text mining

ASJC Scopus subject areas

  • Control and Systems Engineering
  • General Chemistry
  • Geotechnical Engineering and Engineering Geology
  • Mechanical Engineering
  • Metals and Alloys
  • Materials Chemistry

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