Cone-based electrical resistivity tomography
Bibliographic record
Abstract
Abstract Determining the 3D spatial distribution of subsurface properties is a challenging, but critical, part of managing the cleanup of contaminated sites. We have developed a minimally invasive technology that can provide information about the 3D distribution of electrical conductivity. The technique, cone-based electrical resistivity tomography (C-bert), integrates resistivity tomography with cone-penetration testing. Permanent current electrodes are emplaced in the subsurface and used to inject current into the subsurface region of interest. The resultant potential fields are measured using a surface reference electrode and an electrode mounted on a cone penetrometer. The standard suite of cone penetration measurements, including high-resolution resistivity logs, are also obtained and are an integral part of the C-bert method. C-bert data are inverted using an inexact Gauss-Newton algorithm to produce a 3D electrical conductivity map. A majorchallenge with the inversion is the large local perturbation around the measurement location, due to the highly conductive cone. As the cone is small with respect to the total model space, explicit modeling of the cone is cost prohibitive. We have developed a rapid method for solving the forward model which uses iteratively determined boundary conditions (IDBC). This allows us to generate a computationally feasible, preinversion correction for the cone perturbation. We assessed C-bert by performing a field test to image the conductivity structure of the Kidd 2 site near Vancouver, British Columbia. A total of nine permanent current electrodes were emplaced and five C-bert data sets were obtained, resulting in 6516 data points. These data were inverted to obtain a 3D conductivity image of the subsurface. Furthermore, we demonstrated, using a synthetic experiment, that C-bert can yield high quality electrical conductivity images in challenging field situations. We conclude that C-bert is a promising new imaging technique.
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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 | 0.000 | 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 | 0.001 |
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".