Project description
Mapping reservoirs beneath the seafloor
Offshore freshened groundwater (OFG), found below the seafloor with lower salinity than seawater, holds potential as a coastal water resource. However, insufficient data on its distribution impedes effective use to alleviate water stress in coastal regions. In this context, the EU-funded OPTIMAL project aims to predict OFG occurrence and distribution globally. Its methodology combines numerical simulation and machine learning, using geological and geomorphological factors. Led by the University of Malta, with partners Utrecht University and Deltares, the project advances scientific understanding and aligns with Sustainable Development Goals. It facilitates knowledge transfer while enhancing expertise in marine geology and applied machine learning. With real-world data validation, OPTIMAL signifies a significant step towards sustainable water resource management.
Objective
Offshore freshened groundwater (OFG) refers to fluids stored in sediment pores and rock fractures below the seafloor, with a salinity lower than seawater. This phenomenon has been identified globally in continental margins and proposed as a resource that can potentially alleviate water stress in coastal regions. However, the scarcity of data to constrain the distribution and volumes of the reservoirs remains a challenge. OPTIMAL project aims to: (i) develop an interdisciplinary methodology to predict the occurrence and distribution of OFG resources built on Artificial Intelligence and (ii) apply the model globally to infer OFG occurrence and quantify the resource feasibility as a function of distribution characteristics such as offshore extent, depth below the seafloor and fresh to brackish water ratio. The proposed methodology uses a surrogate model to create a dataset of input parameters, representing key geological and geomorphological components influencing OFG systems, such as aquitard thickness and seafloor bathymetry. The output data will be generated via numerical simulation of variable-density groundwater transport on the suite of surrogate models using high-performance computing. These data will be used to train and test machine learning algorithms. The successful models will be validated using real-world data from the existing global OFG database. The predictive model proposed in this fellowship contributes to achieving Sustainable Development Goals related to technologies for improving access to water resources. The primary beneficiary of this funding will be the University of Malta. Partner organizations will be Utrecht University and Deltares in The Netherlands. The action presents a unique opportunity for the fellow to transfer his expertise in stochastic reservoir modelling and characterization of OFG systems to the host, while learning about marine geology, seafloor landforms and applied machine learning.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- natural sciencescomputer and information sciencesdatabases
- natural sciencesearth and related environmental sciencesgeology
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Programme(s)
Funding Scheme
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European FellowshipsCoordinator
2080 L-Imsida
Malta