Earth Resources

The Earth Resources research theme covers many different aspects of Earth science research in the UCD School of Earth Sciences. A secure, sustainable, and efficient supply of water, energy, and minerals is necessary for our society and for the continued development of both the Irish and European economies. A key overarching goal within this research theme is to better understand how these resources form, where they can be found, and in what way they can be safely and sustainably managed and extracted.

Mineral products are critical for the technologies that allow us to deal with environmental change, especially the critical minerals required for electric vehicles and renewable energy systems. Research in the school includes:

mineral exploration and mining of a number of deposit types, especially in sedimentary basins, and
research into societally critical metals that enable the energy transition, and
advancing decision-making processes, to ensure responsible environmental management decisions and responsible extraction and (re)use of Earth’s resources.

Understanding underground reservoirs is crucially important for finding and managing earth resources. Research in the school includes research into:

ore deposits in sedimentary basins,
aquifers for groundwater,
geothermal energy in deep and shallow reservoirs,
hydrocarbon reservoirs, and
depleted reservoirs which may be used as repositories for captured carbon dioxide and other calorific gases.

The Earth Resources research theme builds on significant infrastructure in the School, including the National Centre for Isotope Geochemistry (NCIG), and the Irish Centre for Research in Applied Geosciences (iCRAG). A range of approaches are used, and the school carries out research in methodological development of these techniques, with applications in earth resources. These include 3D geological and volumetric modelling, reservoir studies, structural and stratigraphic analysis, stable and radiogenic isotopes, petrographic, remote sensing and seismic stratigraphy, borehole and subsurface characterisation, core scanning, and social science methodologies, in collaboration with the School of Business.

Recent publications

(opens in a new window)Kaeter, D., Barros, R. and Menuge, J.F., 2021. Metasomatic-hydrothermal HFSE, tin and base metal enrichment processes in lithium pegmatites, SE Ireland, and their relationship to late-orogenic Li-Sn(-W) systems. Economic Geology, 116, 169-198.

(opens in a new window)Melo, A., Li, Y., 2021. Geology differentiation by applying unsupervised machine learning to multiple independent geophysical inversions. Geophysical Journal International227(3):2058-2078.

(opens in a new window)Fonte-Boa, T.M.R., Melo, A.T., and Novo, T.A., 2020. Interpolation artifacts as a result of spatial aliasing: A case study of the airborne magnetic data set of southeastern Minas Gerais, Brazil. Geophysics85(6):B193-B205.

(opens in a new window)Melo, A.T., Li, Y., and Hitzman, M, 2020. Is there hidden potential in Carajás? Insights through the geophysical signature of Cristalino deposit. Ore Geology Reviews 126, 103735.

(opens in a new window)Slezak, P., Spandler, C., Border, A., and Whittock, K., 2021, Geology and ore genesis of the carbonatite-associated Yangibana LREE district, Gascoyne Province, Western Australia. Mineralium Deposita, 56, 1007-1026.

(opens in a new window)Lowney, B., Lokmer, I., O'Brien, G.S., and Bean, C.J., 2021. Pre-migration diffraction separation using generative adversarial networks. Geophysical Prospecting69(5):949-967.

(opens in a new window)Islam M.S., Manzocchi T., Walsh, J.J., and Stern, D., 2021. A comparative study of geometrical upscaling methods for inclusion of fault zone structure into upscaled reservoir simulation models. Natural Resources Research, 30, 2561-2573.

(opens in a new window)Walsh D.A. and Manzocchi T., 2021. Connectivity in Pixel-Based Facies Models. Mathematical Geosciences 53, 415-435.

(opens in a new window)Walsh D.A. and Manzocchi T., 2021. A method for generating geomodels conditioned to well data with high net:gross ratios but low connectivity. Marine and Petroleum Geology, 2021

(opens in a new window)An, Y., Guo, J., Ye, Q., Childs, C., Walsh, J.J. and Dong, R., 2021. Deep convolutional neural network for automatic fault recognition from 3D seismic datasets. Computers & Geosciences, 153, 104776.

(opens in a new window)O'Sullivan, C., Childs, C., Saqab, M.M., Walsh, J.J. and Shannon, P., 2021. The influence of multiple salt layers on rift-basin development; The Slyne and Erris basins, offshore NW Ireland. Basin Research 33(3), 2018-2048.