This project will include in-depth analysis of chemical and biological soil health indicators from Tellus and previous research projects. It will analyse soil organic matter (SOM), soil respiration, autoclaved-citrate extractable (ACE) protein, and active carbon for use as representative biological properties. We will analyse soil pH, phosphorous, potassium, and minor element composition as key chemical characteristics. The data will support complementary research by analysing soils collected in tandem with those for microbial ecology investigation, thus providing confirmation of previously collected Tellus data (e.g., pH, elemental composition). In addition, the proposed project will generate novel datasets of biological and chemical soil health characteristics that can be compared and contrasted with both other data, providing the most detailed investigation of soil health in Ireland to-date.

Lead Applicant: Dr Shane O'Reilly

Scholar: Hayley Buttimer

Host: Atlantic Technological University - Sligo

 This project aims to acquire geospatially corrected photogrammetric data using a combination of low-cost tools such a UAVs and smart phones. These data will be processed using well-developed photogrammetry workflows (e.g. Lim et al., 2020) and be imported to Unity, an Open Source gaming engine, in which a student can navigate across the environment and record/read real geographic measurements (length and thickness of units, strike and dip, orientation, stratigraphic log and core samples). On a broader scale, this project will act as a pilot for a longer-term aim where a catalogue of Irish and internationally recognised virtual fieldtrips will be made to support geoscience teaching in Ireland and abroad.

Lead Applicant: Dr Aaron Lim

Scholar: Irene Loriga

Host: University College Cork

This project aims to create an objective and standardised sediment classification workflow that may be upscaled to large governmental programs. It will apply an enhanced angular range analysis to 6 broad areas of INFOMAR data: deep (+700 m); intermediate (400 m); shallow (200 m) and areas of interest to ORE development ranging from 100 to 20 m. The backscatter grids generated will be segmented and classified based on ARA-derived sediment composition. Magnetometry data will be diurnally corrected and depth-normalised such that total and residual values can be embedded within the classification. Accuracy will be calculated using previously acquired sediment samples and a new workflow document created for the derivation of these data layers that can be adopted by governmental programs and increase industrial uptake of governmental data.

Lead Applicant: Dr Aaron Lim

Scholar: Cara Brennan

Host: University College Cork 

The proposed study will develop a suite of cluster (catchment type)-specific statistical empirical models relating two indices derived from groundwater levels, namely groundwater memory and standardised groundwater level index (SGI), with principal climatic variables and catchment characteristics. A combination of clustering procedures and machine learning algorithms will be used to develop these models which provide a predictive tool to forecast the impacts of climate change on groundwater levels in differing Irish aquifer types and significant individual aquifers. Results will then be extrapolated (upscaled) to represent the spatial and temporal variations of climate impacts across the Republic of Ireland through integration of modelling results with the existing GIS platform.

Lead Applicant: Dr Ahmed Nasr

Scholar: Tarig Mohamed

Host: Technological University Dublin

Despite offering multiple services, including nutrient cycling, water storage and transmission, and pathogen elimination, the ground surface ecosystems have drawn less attention when compared to other aquatic ecosystems. The microorganisms in the groundwater are directly linked to many of these valuable ecosystem services. Although groundwater ecosystems are located beneath the ground, frequent contaminations, changes in temperature regimes, and recharge patterns impact the organisms that live there and the services they offer. Growing industrialization and synthetic chemicals pollute shallow and deep groundwater, while groundwater temperatures rise due to climate change. These threats would change the structure and functioning of groundwater ecosystems. However, the predictions for how climate change would directly affect groundwater systems are very uncertain. According to studies, a twofold increase in CO2 would cause extreme weather, causing temperatures in warmer climate regions to rise by up to 4oC. This project (24 months Research Masters) aims toanalyze the potential hazards caused by groundwater contamination to the groundwater ecosystems at various locations in Ireland using the datasets available in Geological Survey Ireland (GSI) and EPA. Advanced machine learning (ML) models will be developed to predict groundwater quality parameters in Ireland by training and testing datasets of various robust ML algorithms (such as Gradient boosting decision trees, Random Forests, Artificial Neural Networks, and Support Vector Machines) using datasets available in GSI and other national resources, for example, Groundwater Protection Data and EPA reports on groundwater quality. Predictions of developed ML models will be compared to values of existing datasets (with statistical analysis) to derive the performance of ML models in estimating groundwater quality parameters. Relevant scholars, practitioners, local and national policymakers will find the results of this study useful for decision-making and planning better groundwater management in this region.

Lead Applicant: DrMdSalauddin

Scholar:TahmidaNaher Chowdhury

Host: University College Dublin

Geothermal energy will play an important role in achieving CO2 emission reduction targets by 2030. Carboniferous carbonate rock reservoirs in Ireland provide both shallow and deep geothermal targets. The porosity-permeability system in these carbonates is dominated by secondary porosity such as faults, fractures and karst. Despite their importance, the fracture and mechanical stratigraphy is not well understood.

Therefore, this study will analyse fault and fracture stratigraphy of potential onshore carbonate reservoir rocks. It will provide upscaling by integrating regional-, outcrop- and bed-scale observations, comparing surface with subsurface observations.

Field reservoir analogue studies will be conducted in quarries and key outcrops. This includes mapping of lithofacies, bed-scale parameters including thickness and variability, and fracture intensity and patterns. Field mapping will be upscaled through digital outcrop mapping using automated feature extraction on structure-from-motion drone photogrammetry. Present-day mechanical strength and elastic properties will be determined on representative lithologies using rock deformation experiments, to constrain mechanical stratigraphy.

Fracture, lithological and alteration parameters will be extracted from diamond drillcores. They will be compared with on-core petrophysical analysis and existing downhole geophysics, especially sonic, density and image data. Downhole survey, core data and analogue studies will be compared, to investigate the implications of stress-release jointing, bed-parallel slip and depth-related changes in karst development, on associated transmissivity and fluid flow.

Cenozoic N-NW-trending strike-slip faults, key conductive structures, and loci for karst conduits, will be mapped from Tellus airborne EM and magnetic surveys. This will extend previous lineament studies and will be integrated with existing constraints on these structures from mines and quarries. An integrated assessment of the impact on deep geothermal potential will then be performed, in particular of fracture permeability and how it relates to reservoir bulk permeability, and possible implications for critically stressed faults. These results will feed directly into ongoing deep geothermal projects.

Lead Applicant: Koen Torremans

Scholar: Simon Vokes

Host: University College Dublin