Exploring for Offshore Freshened Groundwater: Integrated Geophysical Field Studies
Bibliographic record
Abstract
Offshore freshened groundwater (OFG) may become increasingly of interest, particularly in densely populated coastal regions and on islands. The only physical parameter that changes with seafloor pore water salinity is the subsurface electrical resistivity. Therefore, marine electromagnetic measurements are the only techniques that can be used to explore OFGs. However, due to the diffusive nature of the electromagnetic fields, there is always a whole set of electrical resistivity models that may fit the measured data. Conventional deterministic inversion methods derive a single best-fit model and do not capture the associated uncertainties in the resistivity model. This in turn may lead to misinterpretations about the probability of OFG occurrence, volume estimates, and potential connection to land aquifers. This study employs a trans-dimensional Markov- Chain Monte-Carlo (MCMC) inversion workflow on time-domain controlled source electromagnetic (CSEM) data from Canterbury Bight to estimate uncertainties in electrical resistivity models. By integrating probability density distributions of resistivity with interpreted seismic sections, we derive pore-water salinity probability distributions, enabling us to estimate salinity uncertainties. Our results reveal an OFG body in the center of the study area within silt and fine-sand sediments, suggesting a connection to onshore groundwater. This study demonstrates the effectiveness of using Bayesian inversion on CSEM data to estimate pore-water salinity values and their associated uncertainties.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".