EarthByte Group develops machine learning recipe to find copper-gold deposits along the Andes

In a paper just published in the journal Tectonics, EarthByter and Natural Sciences, University of Sydney alumnus Nathaniel Butterworth and colleagues from the School of Geosciences, University of Sydney and Data61/CSIRO have developed a spatio-temporal machine learning recipe to identify subduction zone tectonic environments in which porphyry copper-gold deposits tend to form. The new approach … Read more…

History and current advances in reconstructing the Earth through deep geological time

Rodinia 1000 Ma

Rodinia 1000 MaTime machine: History and current advances in reconstructing the Earth through deep geological time – an article on Quartz by Steve LeVine. The article is a review of the development of ideas and technologies in reconstructing the Earth through deep time, aimed at understanding supercontinent assembly, breakup and dispersal, starting with Alfred Wegener. The article focusses on research activities in the context of the IGCP 648 project ‘Supercontinent Cycles and Global Geodynamics‘ led by Zheng-Xiang Li. The piece provides some historical context, and highlights the work of a number of leading scientists, postdoctoral researchers and PhD students currently involved in this work.  … Read more…

Ore Geology Reviews – Prospectivity of Western Australian iron ore from geophysical data using a reject option classifier

Prospectivity of Western Australian iron ore from geophysical data using a reject option classifier - figure

Merdith, A. S., Landgrebe, T. C., & Müller, R. D. (2015). Prospectivity of Western Australian iron ore from geophysical data using a reject option classifier. Ore Geology Reviews. http://dx.doi.org/10.1016/j.oregeorev.2015.03.014 Prospectivity of Western Australian iron ore from geophysical data using a reject option classifier Download supplementary material – zip file

Prospectivity of Western Australian iron ore from geophysical data using a reject option classifier

Prospectivity of Western Australian iron ore from geophysical data using a reject option classifier - figure

Prospectivity of Western Australian iron ore from geophysical data using a reject option classifier - figureCitation
Merdith, A. S., Landgrebe, T. C., & Müller, R. D. (2015). Prospectivity of Western Australian iron ore from geophysical data using a reject option classifier. Ore Geology Reviews. http://dx.doi.org/10.1016/j.oregeorev.2015.03.014

Abstract
There has recently been a rapid growth in the amount and quality of digital geological and geophysical data for the majority of the Australian continent. Coupled with an increase in computational power and the rising impor- tance of computational methods, there are new possibilities for a large scale, low expenditure digital exploration of mineral deposits. Here we use a multivariate analysis of geophysical datasets to develop a methodology that utilises machine learning algorithms to build and train two-class classifiers for provincial-scale, greenfield min- eral exploration. … Read more…

Big Data Knowledge Discovery

Big Data Knowledge Discovery is an interdisciplinary research initiative that focuses on the scientific challenges and opportunities presented by the use of the new techniques of data science applied in the natural sciences. This research initiative brings together world class discipline leaders in the data-intensive sciences of Geo Sciences, Life Sciences and Physical Sciences with … Read more…

School of Geosciences Awards Evening winners

Tonight was the annual School of Geosciences Awards Evening held at the Macleay Museum. Congratulations to EarthByters Mike Tetley, Dr Sabin Zahirovic, Andrew Merdith, Sarah MacLeod, Carmen Braz, Luke Hardiman and Serena Yeung for receiving academic and School service awards! Dr Maral Hosseinpour was photographer for the evening and took some brilliant photos!

Ore Geology Reviews – Prospectivity of Western Australian iron ore from geophysical data using a reject option classifier

Merdith, A. S., Landgrebe, T. C., & Müller, R. D. (2015). Prospectivity of Western Australian iron ore from geophysical data using a reject option classifier. Ore Geology Reviews. doi: 10.1016/j.oregeorev.2015.03.014. Prospectivity of Western Australian iron ore from geophysical data using a reject option classifier

Opal exploration research recognised as outstanding highlight

Recent EarthByte research on opal exploration was recognised as an outstanding highlight and reflects the work of many of the group including, Andrew Merdith, Tom Landgrebe, Adriana Dutkiewicz and Patrice Rey. Congratulations to all of the contributors to the project and specifically to John Cannon and Michael Chin, the GPlates developers!

Australian Journal of Earth Sciences – Towards a predictive model for opal exploration using a spatio-temporal data mining approach

Merdith, A. S., Landgrebe, T. C., Dutkiewicz, A., & Müller, R. D. (2013). Towards a predictive model for opal exploration using a spatio-temporal data mining approach. Australian Journal of Earth Sciences, 60(2), 217-229. http://dx.doi.org/10.1080/08120099.2012.754793. Towards a predictive model for opal exploration using a spatio-temporal data mining approach Supplementary data 

From data mining to opal mining

Opal NobbyDocuments
AJES Paper
CG Paper

Opal is Australia’s national gemstone, however most significant opal discoveries were made in the early 1900’s – more than 100 years ago – until recently. Currently there is no formal exploration model for opal, meaning there are no widely accepted concepts or methodologies available to suggest where new opal fields may be found. … Read more…

Computers & Geoscience – Relationships between palaeogeography and opal occurrence in Australia: a data-mining approach

Landgrebe, T. C. W., Merdith, A., Dutkiewicz, A., & Müller, R. D. (2013). Relationships between palaeogeography and opal occurrence in Australia: A data-mining approach. Computers & Geosciences, 56, 76-82. http://dx.doi.org/10.1016/j.cageo.2013.02.002. Download the paper – pdf

Computers & Geoscience – Relationships between palaeogeography and opal occurrence in Australia: a data-mining approach

Landgrebe, T. C. W., Merdith, A., Dutkiewicz, A., & Müller, R. D. (2013). Relationships between palaeogeography and opal occurrence in Australia: A data-mining approach. Computers & Geosciences, 56, 76-82. http://dx.doi.org/10.1016/j.cageo.2013.02.002. Download the paper – pdf