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Big Data Techniques for Applied Geoscience: Compute and Communicate
Citation
Reading, AM and Cracknell, MJ and Kuhn, S, Big Data Techniques for Applied Geoscience: Compute and Communicate, ASEG Extended Abstracts 2016: 25th International Geophysical Conference and Exhibition, 21-24 August 2016, Adelaide, Australia, pp. 581-585. (2016) [Keynote Presentation]
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Official URL: http://conference.aseg.org.au/
DOI: doi:10.1071/ASEG2016ab181
Abstract
Big Data techniques have the potential to be paradigm-changing for applied geoscience if they are used widely. A significant
number of such techniques, under the umbrella of Earth informatics, involve Machine Learning applied to high dimensional data to
create new forms of value. This contribution presents two case studies of successful Earth informatics computation and the
communication of the value of results, which provide insight into the uptake of ‘Big Data’ in geosciences.
Machine Learning techniques split naturally into either supervised or unsupervised approaches. Supervised algorithms, such as
Random Forests (RF), support vector machines or neural networks, share the concept of training a classifier using an initial
(training) dataset. They are generally applied to predictive tasks, such as our first case study, predicting lithology from remote
sensing and airborne geophysical data. Unsupervised algorithms, such as Self-Organising Maps (SOM), allow patterns inherent in
the data to emerge without the use of a training dataset. They are generally applied to tasks which seek to explore patterns in data,
such as our second case study, which identifies new potentially prospective river catchments. We find that calculating and presenting
explicitly the newly extracted value, of the result obtained through computation, is an essential component of the post-compute
evaluation.
As strong advocates for the use of a range of Big Data techniques in applied geosciences, we conclude that the benefits to be gained
from the way that we ‘compute’ can be lost if we do not also take considerable care with the ways that we ‘communicate’.
Item Details
Item Type: | Keynote Presentation |
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Keywords: | Big Data, Machine Learning, Supervised, Unsupervised, High Dimensional, Communication |
Research Division: | Information and Computing Sciences |
Research Group: | Computer vision and multimedia computation |
Research Field: | Pattern recognition |
Objective Division: | Mineral Resources (Excl. Energy Resources) |
Objective Group: | Mineral exploration |
Objective Field: | Mineral exploration not elsewhere classified |
UTAS Author: | Reading, AM (Professor Anya Reading) |
UTAS Author: | Cracknell, MJ (Dr Matthew Cracknell) |
UTAS Author: | Kuhn, S (Mr Stephen Kuhn) |
ID Code: | 111266 |
Year Published: | 2016 |
Deposited By: | CODES ARC |
Deposited On: | 2016-09-07 |
Last Modified: | 2017-12-14 |
Downloads: | 1 View Download Statistics |
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