Imaging of 3D electromagnetic data at low-induction numbers
Bibliographic record
Abstract
ABSTRACT We expressed electromagnetic measurements at low induction numbers as spatial averages of the subsurface electrical conductivity distribution and developed an algorithm for the recovery of the latter in terms of the former. The basis of our approach is an integral equation whose averaging kernel is independent of the conductivity distribution. That is, the recovery of conductivity from the measurements leads to a linear inverse problem. Previous work in one and two dimensions demonstrated that using a kernel independent of conductivity leads to reasonably good results in quantitative interpretations. This study extended the approach to 3D models and to data taken along several profiles over a given area. The algorithm handles vertical and horizontal magnetic dipoles with multiple separations for appropriate depth discrimination. The approximation also handles issues like negative conductivity measurements, which commonly appear when crossing near-surface conductors. This happens particularly when using vertical magnetic dipoles; whose averaging kernel has significant negative weights in the space between the dipoles, something that does not happen for the horizontal dipoles. In general, the more complex the kernel, the more complicated the signature of any given anomaly. This makes qualitative interpretations of pseudosections somewhat difficult when dealing with more than one conductive or resistive body. The algorithm was validated using synthetic data for imaging data from horizontal or vertical coils or from a combination of them. Imaging of field data from a mine tailings site recovered a shallow 3D conductive anomaly associated with the tailings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".