Modeling and inversion of electromagnetic data collected over steel casings: An analysis of two controlled field experiments in Colorado
Bibliographic record
Abstract
Abstract Modeling and inversion of electromagnetic (EM) data contaminated by steel infrastructure remains a numerically challenging task. In this study, we collected controlled-source EM (CSEM) field data at two test sites. First, we conducted a CSEM survey over a dry and abandoned well in eastern Colorado. Next, we collected CSEM data over a test well located at the Colorado School of Mines. The purpose of these experiments was to examine methods to invert EM data contaminated by infrastructure effects and recover undistorted subsurface conductivity models. We used a hybrid approach to model casing effects by considering the conductive metal as a distribution of secondary sources with magnitudes calculated using an approximate layered background. In particular, we applied the method of moments algorithm to calculate the magnitude of the secondary electric dipole sources along the casing. Subsequently, we included these secondary sources in a 3D finite-volume-based forward calculation and used the Gauss-Newton method to invert the contaminated field data. As expected, our preliminary inversion results show that by not considering casing effects, significant artifacts are introduced in the recovered models. Furthermore, we show that such artifacts are reduced significantly through the introduction of casing physics in the forward model, enabling the surrounding subsurface conductivity and corresponding geology to be characterized.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".