Investigating internal magnetic field gradients in aquifer sediments
Bibliographic record
Abstract
ABSTRACT Internal magnetic field gradients in porous materials, if sufficiently large, can be a source of error in nuclear magnetic resonance (NMR) measurements of the transverse relaxation time T2 and the diffusion coefficient D. Given that these measurements can provide information about the pore fluid and the pore geometry, it is important to determine the magnitude of internal gradients and assess their potential impact. We estimated the effective internal gradients in aquifer sediment samples using three methods. We used a 2D NMR method to map the distribution of internal gradients versus T2 and found gradients up to 1000 G/cm with peak gradient values in the range of 20−100 G/cm for most of the samples. The average effective gradient values, calculated from the slope of the mean log relaxation rate versus the squared echo time, typically fell above the peak gradient values in the 2D distributions, with a range from 12 to 230 G/cm. The maximum effective gradients, calculated from the magnetic susceptibility of the samples, were found to be the upper bounds for most of the gradient distributions. The mean gradient was found to increase with increasing magnetic susceptibility of the sample; however, pore size was also found to impact gradient magnitudes. Given that the distribution of internal gradient magnitudes is determined by the properties of the sediment and by the magnitude of the background field, our results have implications for the acquisition of logging and surface NMR data. We expect the internal gradients in many aquifer sediments to impact NMR logging measurements; this should be considered when selecting logging parameters and interpreting NMR logging data. In contrast, we expect internal gradients to have a negligible impact on surface NMR measurements because of the much smaller magnitude of the background magnetic field.
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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 | 0.000 |
| 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 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".