Exploiting the MRS‐phase information to enhance detection of masked deep aquifers: examples from the Netherlands
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it