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Combining machine learning and geophysical inversion for applied geophysics
Citation
Reading, AM and Cracknell, MJ and Bombardieri, DJ and Chalke, T, Combining machine learning and geophysical inversion for applied geophysics, ASEG-PESA 2015, 15-18 February 2015, Perth, Australia, pp. 1-4. (2015) [Refereed Conference Paper]
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DOI: doi:10.1071/ASEG2015ab070
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.
Item Details
Item Type: | Refereed Conference Paper |
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Keywords: | machine learning, data mining, modelling and inversion, lithology, 3D geological mapping |
Research Division: | Earth Sciences |
Research Group: | Geophysics |
Research Field: | Geophysics not elsewhere classified |
Objective Division: | Expanding Knowledge |
Objective Group: | Expanding knowledge |
Objective Field: | Expanding knowledge in the earth sciences |
UTAS Author: | Reading, AM (Professor Anya Reading) |
UTAS Author: | Cracknell, MJ (Dr Matthew Cracknell) |
ID Code: | 100296 |
Year Published: | 2015 |
Deposited By: | Earth Sciences |
Deposited On: | 2015-05-10 |
Last Modified: | 2017-11-02 |
Downloads: | 0 |
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