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
Bibliographic record
Abstract
Abstract The 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".