1887
Volume 16, Issue 6
  • ISSN: 1569-4445
  • E-ISSN: 1873-0604

Abstract

ABSTRACT

Planning, contracting, data acquisition and processing plus the inverter's quality assessment and inversion of a regional airborne electromagnetic survey may take some months, while the interpretation of the results is a considerably more complex and comprehensive process. Most often an interpretation necessitates additional data that are time consuming to collect and complicated to integrate into an overall model, for example borehole logs, borehole core samples, water chemistry, surface vegetation, satellite imagery plus all existing geological background knowledge. Interpretation basically has to do with identifying categories and finding boundaries between them so that depths, thicknesses and a whole range of other model attributes can be estimated, qualitatively and/or quantitatively. I present two methods using the continuous wavelet transform of finding attributes intended to assist the interpreter: one finds layer boundaries in the smooth multi‐layer models that are most often used in the inversion of large airborne electromagnetic data sets, and the other finds the natural categories of the model parameter. Naturally, being based on the subsurface conductivity distribution, the boundaries and categories suggested are useful only to the extent that they coincide with geological/hydrogeological boundaries and categories – which is for the interpreter to decide.

Loading

Article metrics loading...

/content/journals/10.1002/nsg.12018
2018-11-12
2024-04-19
Loading full text...

Full text loading...

References

  1. ChambersJ.E., WilkinsonP.B., PennS., WellerA.L., OgilvyR.D., KurasO., et al. 2010. Bedrock interface detection for sand and gravel mineral reserve assessment using 3D ERT. Near Surface 2010 – 16th European Meeting of Environmental and Engineering Geophysics, September 6–8, 2010, Zurich, Switzerland.
  2. ChandrasekharE. and RaoV.E.2012. Wavelet analysis of geophysical well‐log data of Bombay offshore basin, India. Mathematical Geoscience44, 901–928.
    [Google Scholar]
  3. ChristensenN.B.2016a. Fast approximate 1D modelling and inversion of transient electromagnetic data. Geophysical Prospecting64, 1620–1631.
    [Google Scholar]
  4. ChristensenN.B.2016b. Strictly horizontal lateral parameter correlation for 1D inverse modelling of large data sets. Near Surface Geophysics14, 391–399.
    [Google Scholar]
  5. CooperG.R.J. and CowanD.2009. Blocking geophysical borehole log data using the continuous wavelet transform. Exploration Geophysics40, 233–236.
    [Google Scholar]
  6. CorduaK.S., HansenT.M., GulbrandsenM.L., BarnesC. and MosegaardK.2016. Mixed‐point geostatistical simulation: a combination of two‐ and multiple‐point geostatistics. Geophysical Research Letters43, 9030–9037.
    [Google Scholar]
  7. CowanD.R. and CooperG.R.J.2003. Wavelet analysis of detailed drillhole magnetic susceptibility data, Brockman Iron Formation, Hamersley Basin, Western Australia. Exploration Geophysics34, 87–92.
    [Google Scholar]
  8. CracknellM.J. and ReadingA.M.2014. Geological mapping using remote sensing data: a comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information. Computers & Geosciences63, 22–33.
    [Google Scholar]
  9. DavisA.C. and ChristensenN.B.2013. Derivative analysis for layer selection of geophysical borehole logs. Computers & Geosciences60, 34–40.
    [Google Scholar]
  10. GulbrandsenM.L., CorduaK.S., BachT. and HansenT.M.2017. Smart interpretation—automatic geological interpretations based on supervised statistical models. Computational Geoscience21, 427–440.
    [Google Scholar]
  11. HillE.J. and UvarovaY.2018. Identifying the nature of lithogeochemical boundaries in drill holes. Journal of Geochemical Exploration, 184, 167–178.
    [Google Scholar]
  12. HsuH.‐L., YanitesB.J., ChenC.‐C. and ChenY.‐G.2010. Bedrock detection using 2D electrical resistivity imaging along the Peikang River, central Taiwan. Geomorphology114, 406–414.
    [Google Scholar]
  13. JainA.K.2010. Data clustering: 50 years beyond K‐means. Pattern Recognition Letters31, 651–666.
    [Google Scholar]
  14. KotsiantisS.B.2007. Supervised machine learning: a review of classification techniques. Informatica31, 249–268.
    [Google Scholar]
  15. LawrieK., BrodieR.S., MageeJ., TanK., HalasL., MuellerN., et al. 2016. An inter‐disciplinary approach to airborne electromagnetics (AEM) survey design for groundwater exploration using the Australian geoscience data cube and morphotectonics. ASEG‐PESA‐AIG 2016, 25th Conference and Exhibition, Adelaide, Australia.
  16. LawrieK., ChristensenN.B., BrodieR.S., AbrahamJ., HalasL., TanK., et al. 2015. Optimizing airborne electromagnetic (AEM) inversions for hydrogeological investigations using a transdisciplinary approach. ASEG_PESA, 24th International Conference and Exhibition, 2015, Perth Western Australia.
  17. LawrieK., MurrayT., BrodieR.S., GibsonD., HalasL., SymingtonN., et al. 2017. An inter‐disciplinary, multi‐physics approach for rapid mapping and hydrogeological characterisation of neogene intra‐plate fault systems in depositional landscapes. Symposium on the Application of Geophysics to Engineering and Environmental Problems 2017. http://doi.org/10.4133/SAGEEP.30‐050.
    [Google Scholar]
  18. LindebergT. and BretznerL.2003. Real‐time scale selection in hybrid multi‐scale representations. International Conference on Scale‐Space Theories in Computer Vision, 148–163.
    [Google Scholar]
  19. MallatS.1998. A Wavelet Tour of Signal Processing. Academic Press.
    [Google Scholar]
  20. SørensenK.I. and LarsenF.1999. Ellog auger drilling: 3‐in‐one method for hydrogeological data collection. Ground Water Monitoring & Remediation19, 97–101.
    [Google Scholar]
  21. WuJ.2012. Advances in K‐Means Clustering—A Data Mining Thinking. Springer.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.1002/nsg.12018
Loading
/content/journals/10.1002/nsg.12018
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): Airborne EM; Interpretation; Inversion; Wavelet transform

Most Cited This Month Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error