3D inversion for multipulse airborne transient electromagnetic data
Bibliographic record
Abstract
ABSTRACT Multipulse airborne transient electromagnetic (ATEM) systems transmit one high-power pulse and one low-power pulse containing more high-frequency EM signals. Such systems have better near-surface resolutions while maintaining the depth of exploration of other conventional systems. ATEM systems are especially suitable for geologic mapping and mineral exploration. The inversion of multipulse ATEM data has been mainly limited to 1D modeling, which is not suitable for complex underground structures. We have investigated an algorithm for 3D multipulse ATEM data inversion based on direct Gauss-Newton optimization with quite-fast convergence. The forward problems were solved in the frequency-domain based on the secondary scattered electrical field equation, and then the inverse Fourier transform and the convolution with transmitting waveform were applied to calculate the arbitrary waveform response and sensitivity matrix in the time domain. To optimize the number of computations and memory, we further used an EM “footprint” concept in our inversions to reduce the forward model size and sparse the sensitivity matrix. The inversion results of synthetic data showed that our 3D algorithm is very effective for inverting the multipulse data with results combining advantageous resolutions of different transmitting pulses. Finally, we applied our algorithm to invert real survey data obtained at McMurray, Alberta, Canada, to further test its effectiveness.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".