VTEM ET: An improved helicopter time-domain EM system for near surface applications
Bibliographic record
Abstract
Sampling the earliest possible transient EM decay in time-domain airborne electromagnetic data (TDEM) is critical for shallow near surface applications. In an effort to further improve near-surface resolution, starting in late 2015 and into 2016, Geotech continued by developing its new VTEM ET system that uses a re-designed broadband receiver sensor, a re-configured transmitter system, and a new digital acquisition system to achieve precise, distortion free measurements of the time-domain EM decay as early as 0.005 msec after the transmitter turn-off.The new receiver features a much larger frequency bandwidth for lower distortion measurements. The new transmitter delivers a sufficiently high dipole moment, a long pulse-width and faster turn-off time than previous systems, but similarly using a single transmitter pulse. The new digital acquisition system operates at a much higher sampling rate, with significantly more decay channels, particularly in early times, and with low noise levels. The result is a new category of VTEM system that is specifically is designed for precise near-surface applications, such as groundwater and environmental problems, but also with sufficient depth of investigation.We present forward modelling and field survey test results comparing the VTEM ET system with our standard VTEM Plus system with full-waveform processing over a groundwater project with ground geophysical and borehole controls in the upper 30-50 metres..
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".