Improved ERT imaging with 3-D surface-to-horizontal borehole configurations: relevance to dense non-aqueous phase liquids
Bibliographic record
Abstract
SUMMARY Accurate characterization and monitoring strategies are essential for designing and implementing remedial programs for sites polluted with dense non-aqueous phase liquids (DNAPLs). Electrical resistivity tomography (ERT) is a widely used geophysical technique for mapping subsurface features and processes of interest, and exhibits desirable characteristics for DNAPL sites due to its ability to gather large volumes of continuous subsurface information in a non-invasive, cost-effective and time-efficient manner. However, ERT measured only from the surface suffers from poor imaging quality with depth. Enhanced ERT imaging can be obtained via electrodes deployed on the surface and within horizontal boreholes, but so far it has only been investigated for 2-D imaging. This study evaluates the potential of 3-D surface-to-horizontal borehole (S2HB) ERT configurations for imaging 3-D DNAPL source zones. Laboratory tank experiments were first conducted with a 3-D S2HB ERT configuration, which consisted of a surface grid and a single borehole line of electrodes, being used to monitor DNAPL migration within porous media. Results demonstrate that 3-D S2HB ERT with a single borehole provides improved sensitivity at depth, and therefore enhanced imaging compared to conventional 3-D surface ERT. Further tank experiments were performed to assess the performance of single borehole S2HB ERT when (i) the distance between surface and borehole is increased, and (ii) additional horizontal boreholes are included. The S2HB ERT with a single borehole significantly outperforms surface ERT at larger depths, and performs comparably to S2HB ERT using multiple boreholes. This study suggests that 3-D S2HB ERT with a single borehole can provide the enhanced imaging ability needed to map DNAPLs, while also being relatively practical for implementation at field sites.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".