1887
24th International Geophysical Conference and Exhibition – Geophysics and Geology Together for Discovery
  • ISSN: 2202-0586
  • E-ISSN:

Abstract

Machine learning and geophysical inversion both represent ways that the applied geophysicist might gain knowledge from field observations and remote sensed data. The two approaches represent contrasting philosophies based respectively on statistics and physics. Both potentially add insights which might help constrain 3D geology by geophysical means. Machine learning uses patterns in data to provide statistically controlled predictions, e.g. of lithology. In contrast, geophysical inversion relies on modelling the physical response of 3D geological block geometry in a deterministic manner. Although both approaches are widely used, it is not currently commonplace in applied geosciences to make use of a combined approach.

We present an example which aims to refine the 3D geology in a prospective region of west Tasmania. Although the region is geologically well-mapped, thick vegetation and significant topography present a challenging set of conditions under which to refine the lithology and block geometry to a level of detail which will support the next generation of exploration. We use multiple layers of remote sensed geophysical data to provide probabilistic information on near-surface lithology extent using the Random Forests classifier. We show how the statistical, robust, output from the machine learning exercise can be used to guide the construction of improved volume geometry within a 3D GOCAD geological and geophysical modelling environment. This enables better constraints to be supplied to the geophysical inversion with resulting improvements in the detail of the 3D geology.

© 2015 ASEG
Loading

Article metrics loading...

/content/journals/10.1071/ASEG2015ab070
2015-12-01
2026-01-21

  • From This Site

    /content/journals/10.1071/ASEG2015ab070
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Aster, R.C., Borchers, B. and Thurber, C.H., 2013, Parameter Estimation and Inverse Problems, 2nd ed. Academic Press, Elsevier, MA, USA.
  2. Breiman, L., 2001, Random Forests. Machine Learning45, 532.
  3. Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J., 1984, Classification and Regression Trees, The Wadsworths & Brooks/Cole Statistics/Probability Series, Pacific Grove, USA.
  4. Cracknell, M.J., and Reading, A.M., 2014, Geological Mapping using remote sensing data: a comparison of five machine learning algorithms, their response to variation in the spatial distribution of training data and the use of explicit spatial information, Computers and Geosciences,63, 22-33.
  5. Cracknell, M.J., Reading, A.M and McNeill, A.W., 2014, Mapping geology and volcanic-hosted massive sulphide alteration in the Hellyer-Mt Charter region, Tasmania, using Random Forests and Self-Organising Maps, Australian Journal of Earth Sciences,61, 287-304.
  6. Duffett, M., Bombardieri, D., and Chalke, T., 2013. Next generation 3D geological and geophysical modelling, west Tasmania. ASEG-PESA 2013, doi:10.1071/ASEG2013ab246.
  7. Fullagar, P.K., Pears, G.A., Hutton, D., and Thompson A., 2004, 3D Gravity and Aeromagnetic Inversion for MVT Lead-Zinc Exploration at Pillara, WA: Expl. Geophys.,35, 142-146.
  8. Fullagar, P.K., Pears, G.A. and McMonnies, B., 2008. Constrained inversion of geological surfaces - pushing the boundaries. The Leading Edge,27, 98-105.
  9. Fullagar, P.K. and Pears, G.A., 2007, Towards geologically realistic inversion: in Proceedings of Exploration 07: Fifth Dec. Int. Conf. on Min. Expl., B. Milkereit (ed.), 444-460.
  10. Hastie, T., Tibshirani, R., Friedman, J.H., 2009, The elements of statistical learning: data mining, inference and prediction, 2nd ed, Series in Statistics. Springer, New York, USA. Kuhn, M., 2014. caret: Classification and Regression Training.
  11. Liaw, A., Wiener, M., 2002. Classification and Regression by randomForest. RNews2, 18-22.
  12. McGaughey, J., 2006. The Common Earth Model: A Revolution in Mineral Exploration Data Integration: in GIS Applications in the Earth Sciences, Geological Association of Canada Special Publication44, ed. Harris, J.R., 567-576. Mineral Resources Tasmania, 2011. 1:25,000 Scale Digital Geology of Tasmania.
/content/journals/10.1071/ASEG2015ab070
Loading
  • Article Type: Research Article
Keyword(s): 3D Geological mapping; Data mining; Lithology; Machine learning; Modelling and inversion

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