Characterizing Offshore Freshened Groundwater Salinity Patterns Using Trans‐Dimensional Bayesian Inversion of Controlled Source Electromagnetic Data: A Case Study From the Canterbury Bight, New Zealand

Author:

Faghih Zahra1ORCID,Haroon Amir12ORCID,Jegen Marion1,Gehrmann Romina3ORCID,Schwalenberg Katrin4ORCID,Micallef Aaron5,Dettmer Jan6ORCID,Berndt Christian1ORCID,Mountjoy Joshu7ORCID,Weymer Bradley A.8

Affiliation:

1. GEOMAR Helmholtz Centre for Ocean Research Kiel Germany

2. Hawai'i Institute of Geophysics and Planetology University of Hawai'i at Manoa Honolulu HI USA

3. Faculty of Computer Science Dalhousie University Halifax NS Canada

4. Federal Institute for Geosciences and Natural Resources (BGR) Hanover Germany

5. Monterey Bay Aquarium Research Institute Moss Landing CA USA

6. University of Calgary Calgary AB Canada

7. National Institute for Water and Atmospheric Research (NIWA) Wellington New Zealand

8. School of Oceanography Shanghai Jiao Tong University Shanghai China

Abstract

AbstractThe study of offshore freshened groundwater (OFG) is gaining importance due to population growth and environmental pressure on coastal water resources. Marine controlled source electromagnetic (CSEM) methods can effectively map the spatial extent of OFG systems using electrical resistivity as a proxy. Integrating these resistivity models with sub‐surface properties, such as host‐rock porosity, allows for estimates of pore‐water salinity. However, evaluating the uncertainty in pore‐water salinity using resistivity models obtained from deterministic inversion approaches presents challenges, as they provide only one best‐fit model, with no associated estimate of uncertainty. To address this limitation, we employ trans‐dimensional Markov‐Chain Monte‐Carlo inversion on marine time‐domain CSEM data, acquired in the Canterbury Bight, New Zealand. We integrate resistivity posterior probability distributions with borehole and seismic reflection data to estimate pore‐water salinity with corresponding uncertainty estimates. The results highlight a low‐salinity groundwater body in the center of the survey area, hosted by consecutive silty‐ and fine‐sand layers approximately 20–60 km from the coast. The posterior probability distribution of resistivity models indicates freshening of the OFG body toward the shoreline within a permeable, coarse‐sand layer 40–150 m beneath the seafloor, suggesting an active connection between the OFG body and the terrestrial groundwater system. The approach demonstrates how Bayesian inversion constrains the uncertainties in resistivity models and subsequently in pore‐water salinity estimates. Our findings highlight the potential of Bayesian inversion to enhance our understanding of OFG systems and provide uncertainty constraints for hydrogeological modeling, thereby contributing to sustainable water resource development.

Funder

HORIZON EUROPE European Research Council

Publisher

American Geophysical Union (AGU)

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