Position: Research Assistant
To a certain extent I stumbled upon the field of environmental radioactive given I have come from a broad chemistry background. Nonetheless, I have found my niche within signal processing developing novel algorithms, often incorporating artificial intelligence, to process gamma-ray spectra taken within the environment; predominantly from contaminated land sites. More recently, I have been utilising similar techniques to estimate soil erosion on ploughed fields and assess the spatial variation of natural radiation. In summary, I enjoy anything that involves large amounts of data using various methods to filter, reduce noise and statistically analyse that data with the ultimate goal of producing interpretable quantities to aid in environmental investigations.
Varley A., Tyler A., Dowdall M. Bondar Y. and Zabrostski V. (2017) An in situ method for the high resolution mapping of 137Cs and estimation of vertical depth penetration in a highly contaminated environment, Science of the Total Environment, 605–606, 957–966
Varley A., Tyler A., Smith L., Dale P. and Davies M. (2016) Mapping the spatial distribution and activity of 226Ra at legacy sites through Machine Learning interrogation of gamma-ray spectroscopy data, Science of the Total Environment, 545-546, 654-661
Varley A., Tyler A., Smith L., Dale P. and Davies M. (2015) Remediating radium contaminated legacy sites: Advances made through machine learning in routine monitoring of “hot” particles, Science of the Total Environment, 521–522, 270–279
Varley A., Tyler A., Smith L. and Dale P. (2015) Development of a neural network approach to characterise 226Ra contamination at legacy sites using gamma-ray spectra taken from boreholes, Journal of Environmental Radioactivity, 140, 130- 140