The interferographic TEM (ITEM) method: Array beamforming for TEM field compaction and resolution improvement
Bibliographic record
Abstract
A new method of array processing, called Interferographic transient electromagnetics (ITEM), of semi-airborne multi-source, multi-receiver TEM data is introduced using beamforming techniques to synthetically form impulsive distributions of TEM fields that partially unmix diffusive EM field structure and improve resolution of subsurface geoelectric structure. ITEM differs from applications of the wave propagation synthetic aperture concept to diffusive EM geophysics. The synthetic aperture concept is incomplete, achieving no vertical compaction, for TEM beamforming. ITEM achieves full beamforming by using both spatial and temporal sets of subsurface electric field distributions to form a two-dimensional (2D) digital filter. Significant impulsive EM field compaction in both horizontal and vertical dimensions results. ITEM is described for a 2D geometry with a semi-airborne survey design. ITEM processing is applied to a reference model set of electric field distributions as well as both reference model and acquired magnetic field profiles. The resulting filtered distributions are quickly translated into a subsurface resistivity image using a simple image formation process. An example for a synthetic geoelectric structure is provided that demonstrates ITEM processing and subsurface imaging.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".