Characterizing Spatial Heterogeneity of Hydraulic Conductivity using Borehole NMR in a Complex Groundwater Flow System
Bibliographic record
Abstract
Borehole nuclear magnetic resonance (NMR) measurements can map hydraulic conductivity (K) distributions in unconsolidated sediments. Previous studies focused on establishing petrophysical models relating NMR responses to K and calibrating model constants for optimized K estimation. However, research has yet to explore the potential of NMR measurements to generate spatial K distributions, which would enable its utilization in numerical groundwater flow and transport models. In this study, we construct various spatial K models based on borehole NMR signals. Quantitation of spatial heterogeneity between NMR logs is explored using 1) geostatistical interpolation approaches, including ordinary kriging and indicator kriging, 2) a zonation approach using clustering with spatial constraints for improved extraction of zone geometry, and 3) a hybrid model of multi-level spatial heterogeneity comprised of both interlayer and intralayer variations of K. The hydraulic responses associated with NMR-derived spatial K models are evaluated using ten pumping tests by comparing the predicated drawdown response for each model configuration to field-observed drawdowns and the model replication of a permeameter-derived K profile at an unsampled location. Results indicate that cross-hole K variations can be accurately generated from NMR logs, yielding reliable drawdown predictions based on a direct comparison with permeameter-derived K models. Here, the hybrid multi-heterogeneity model approach achieved optimal prediction performance. This study provides a framework for using high-resolution NMR-derived K profiles from multiple boreholes to characterize spatial heterogeneity at sub-meter scales in complex glacial deposits.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".