Mapping and Monitoring of DNAPL Source Zones With Combined Direct Current Resistivity and Induced Polarization: A Field‐Scale Numerical Investigation
Bibliographic record
Abstract
Abstract Direct current (DC) resistivity has been widely investigated for non‐invasive mapping of dense non‐aqueous phase liquids (DNAPLs); however, due to its difficulty in distinguishing DNAPLs from adjacent soils, the DC method is limited for static detection of DNAPLs and more often employed for monitoring DNAPL changes over time. Time‐domain induced polarization (IP) can provide complementary information to better discriminate between DNAPL, water and surrounding soils. Since highly resistive DNAPLs tend to laterally spread and pool on polarizable (chargeable) clay lenses, combined DC and IP (DCIP) has the potential to enhance static detection, and also monitoring of DNAPL source zones (SZs). The objective of this study is to assess DCIP for characterizing and monitoring DNAPL SZs at the field‐scale. A new DNAPL‐DCIP numerical model was first developed that couples a 3D multiphase flow model, which simulates DNAPL release and remediation scenarios, with a 3D DCIP model, which calculates the corresponding resistivity and chargeability response. The sensitivity of the DCIP response to key DNAPL and soil properties was then analyzed at a single subsurface location, closely matching previous theoretical and experimental observations. Finally, a field‐scale simulation of DNAPL release and remediation was conducted, with simultaneous mapping by DCIP surveys. Results demonstrate that chargeability can provide enhanced understanding of the lithological distribution that controls the variability in the DNAPL SZ, with time‐lapse resistivity being used to monitor DNAPL mass changes during SZ remediation. This numerical study suggests that combined DCIP can be valuable for site characterization and time‐lapse monitoring performance at DNAPL‐impacted sites.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".