Automatic mapping of the base of aquifer - a case study from Morrill, Nebraska

Research output: Contribution to journalJournal articleResearchpeer-review

  • Mats Lundh Gulbrandsen
  • Lyndsay B. Ball
  • Burke J. Minsley
  • Thomas Mejer Hansen

When a geologist sets up a geologic model, various types of disparate information may be available, such as exposures, boreholes, and (or) geophysical data. In recent years, the amount of geophysical data available has been increasing, a trend that is only expected to continue. It is nontrivial (and often, in practice, impossible) for the geologist to take all the details of the geophysical data into account when setting up a geologic model. We have developed an approach that allows for the objective quantification of information from geophysical data and borehole observations in a way that is easy to integrate in the geologic modeling process. This will allow the geologist to make a geologic interpretation that is consistent with the geophysical information at hand. We have determined that automated interpretation of geologic layer boundaries using information from boreholes and geophysical data alone can provide a good geologic layer model, even before manual interpretation has begun. The workflow is implemented on a set of boreholes and airborne electromagnetic (AEM) data from Morrill, Nebraska. From the borehole logs, information about the depth to the base of aquifer (BOA) is extracted and used together with the AEM data to map a surface that represents this geologic contact. Finally, a comparison between our automated approach and a previous manual mapping of the BOA in the region validates the quality of the proposed method and suggests that this workflow will allow a much faster and objective geologic modeling process that is consistent with the available data.

Original languageEnglish
JournalInterpretation
Volume5
Issue number2
Pages (from-to)T231-T241
Number of pages11
ISSN2324-8858
DOIs
Publication statusPublished - 2017

    Research areas

  • airborne survey, aquifer, artificial intelligence, interpretation, mapping

ID: 197797449