Exploiting the MRS‐phase information to enhance detection of masked deep aquifers: examples from the Netherlands
Bibliographic record
Abstract
ABSTRACT Several magnetic resonance soundings (MRS) in the Netherlands showed a monotonous single peak anomaly on the amplitude versus excitation moment sounding pattern, which were interpreted as a single aquifer when using an amplitude‐only mode MRS data inversion. However, in all these soundings, borehole logs documented the presence of two or three aquifers separated by clay‐rich aquitards in the first 100 m below ground surface. Such environments were electrically conductive so a phase excursion was noticeable on the MRS soundings. Multi‐aquifer systems, in a conductive environment, may show interference among signals originating from different parts of the systems including amplitude masking or destructive interference. A new version of an off‐the‐shelf MRS forward modelling and inversion tool (Samovar 11.3) allowing complex amplitude and phase inversion was used to detect and parameterize deep, MRS‐masked second aquifers at two selected sites in the Netherlands, one near Delft and one near Waalwijk. At the Delft site, the proposed strategy was effective in the detection and characterization of a second previously missed aquifer at 45 m below ground surface, while at the Waalwijk site, the second aquifer was not detected because of a considerably deeper aquifer at 85 m and too small excitation (6000 A ms). However, forward modelling showed that with a larger excitation moment (e.g., 13 000 A ms), detection and parameterization of the second aquifer would become possible when using both amplitude and phase.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".