Two- and three-dimensional electrical resistivity imaging at a heterogeneous remediation site
Bibliographic record
Abstract
Abstract Geometrically complex heterogeneities at a decommissioned sour gas plant could not be adequately characterized with drilling and 2D electrical resistivity surveys alone. In addition, 2D electrical resistivity imaging profiles produced misleading images as a result of out-of-plane resistivity anomalies and violation of the 2D assumption. Accurate amplitude and positioning of electrical conductivity anomalies associated with the subsurface geochemical distribution were required to effectively analyze remediation alternatives. Forward and inverse modeling and field examples demonstrated that 3D resistivity images were needed to properly reconstruct the amplitude and geometry of the complex resistivity anomalies. Problematic 3D artifacts in 2D images led to poor inversion fits and spurious conductivity values in the images at depths close to the horizontal offset of the off-line anomaly. Three-dimensional surveys were conducted with orthogonal sets of Wenner and dipole–dipole 2D resistivity survey lines. The 3D inversions were used to locate source zones and zones of elevated ammonium. Thus, conducting 3D electrical resistivity imaging (ERI) surveys early in the site characterization process will improve cost effectiveness at many remediation sites.
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.
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 | 0.000 |
| 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) | 0.001 | 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".