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Tom Horrocks

PhD student
Centre for Exploration Targeting (CET)

Contact details

Address
Robert Street Building, Rm 107
Centre for Exploration Targeting (CET)
The University of Western Australia (M006)
35 Stirling Highway
CRAWLEY WA 6009
Australia

Phone
+61 8 6488 1878

Email
tom.horrocks@research.uwa.edu.au

Tom Horrocks commenced as a PhD candidate with the CET in September 2014 to work on methods for robust geological modelling based on automated analysis of wireline logs. The project’s primary aim is to devise methods for extrapolating downhole petrophysical data, which has been correlated with lithology and alteration by automated methods, to the lithology and alteration in a three-dimensional volume occupied by multiple inverted geophysical datasets. Hurdles that must be overcome with this approach include methods for correlation, handling the difference in scales between downhole petrophysics and the three-dimensional inverted volumes, and quantification of the uncertainty associated with lithology extrapolation. Tom is supported by a top-up scholarship from First Quantum Minerals, who are also providing geophysical data from the Kevitsa Ni-Cu-PGE deposit in northern Finland.
 

Tom completed a BEng. (Software) and BSc. (Physics, Applied Mathematics) at UWA in mid-2013, graduating with first class honours in engineering after completing his thesis on coal strata classification and well log correlation, which was jointly supervised by CET’s Eun-Jung Holden and Daniel Wedge and Conducive’s Darren Christophersen. Tom has worked as a Software Engineer in industry with Empired Applications & Consulting (formerly Conducive) as both a student and graduate (2011-2014). He has extensive experience in Microsoft technologies (e.g. C#, SQL Server, Silverlight, SSRS), but also enjoys coding in Java, C, and a slew of scripting languages. His work has varied widely between data migration, new system development, maintenance, and performance profiling. In his free time Tom can be found playing basketball and reading about Machine Learning.