Model reliability for 3D electrical resistivity tomography: Application of the volume of investigation index to a time-lapse monitoring experiment
Bibliographic record
Abstract
Abstract Solution appraisal is difficult for large 3D, nonlinear inverse problems such as electrical resistivity tomography (ERT). We construct the volume of investigation index (VOI) as the sensitivity of the inversion result to a variable-reference model. This limited exploration of the model space provides an efficient and pragmatic method of appraisal for a particular data set and a 3D model domain. We present a synthetic example to demonstrate the applicability of the VOI as a tool for characterizing model reliability for 3D ERT and as a method of survey design. We show how the VOI provides a measure of model resolution and how insight gained from VOI analysis cannot be gained through similar examination of the average sensitivity distributions. In the context of ERT monitoring of an injection/withdrawal experiment, we utilize the VOI for judging the degree of reliability of hydrogeological interpretations that stem from features observed in the estimated electrical-conductivity models. We employ the VOI for the experimental data as a comparative measure of survey performance. For this experiment, the VOI shows that a larger, more artifact-free region of reliability is achieved using a circulating vertical dipole-dipole survey geometry, as opposed to a horizontal dipole-dipole survey geometry. The experimental VOI distributions exhibit dependence on the borehole infrastructure and the actual earth model.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".