Resolution of 3-D Electrical Resistivity Images from Inversions of 2-D Orthogonal Lines
Bibliographic record
Abstract
Abstract Three-D electrical resistivity imaging (ERI) using sets of orthogonal of 2-D survey lines provides an efficient and cost effective tool for site characterization in environmental and engineering investigations. A 3-D survey design using sparse sets of lines reduces the survey time at the expense of the resolution. The effects of line spacing on the resolution of 3-D electrical resistivity images were investigated using numerical modeling with synthetic and field data for two standard configurations, dipole-dipole and Wenner arrays. Synthetic data studies indicate that dipole-dipole configuration produces a more accurate map of the subsurface than the Wenner configuration. A severely under-sampled 3-D survey could result in introducing small-scale shallow spurious artifacts at the surface of the resistivity model caused by the projection of the anomalies located in the deeper parts of the model. Results from inversion of the real and synthetic data showed that lines should be separated by no more than four electrode spacings and, if the shallow subsurface is important, by no more than two electrode spacings. The dipole-dipole array performs better than the Wenner array, but it requires more acquisition effort and is more sensitive to noise. These modeling results provide insight into quantitative survey designs that produce sufficient information to meet survey objective within a given field efforts.
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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 | 0.001 | 0.003 |
| 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 | 0.000 | 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".