Compensating for temperature variations in time-lapse electrical resistivity difference imaging
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Variations in temperature during time-lapse electrical resistivity imaging (ERI) surveys introduce changes in electrical conductivity (EC). When the goal of the time-lapse ERI survey is to image changes in EC due to changes in saturation or pore water salinity, compensation must be made for the effect of temperature variations. A temperature-compensation method can approximate time-lapse ERI data with the effect of temperature variations removed. First uncompensated ERI data are inverted. The inversion model then is adjusted to a standard temperature image. Forward simulations are performed using the uncompensated inversion and the standard temperature equivalent model. The temperature-compensated simulated resistance data are subtracted from the uncompensated simulated resistance data, forming data correction terms. The data correction terms then are subtracted from the measured data to yield temperature-compensated data. Using the temperature-compensated data, inversions have been carried out on two synthetic data sets and a field example. Differencing two temperature-compensated data inversions is found to be superior to differencing two postinversion standard temperature equivalent images. Temperature compensation on the data allows temperature corrections to be applied to time-lapse difference inversion schemes and hydrogeophysical inversion where postinversion temperature-correction methods are not easily applied.
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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 | 0.001 |
| 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 it