3D inversion for multipulse airborne transient electromagnetic data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it