The Fourier transform of controlled-source time-domain electromagnetic data by smooth spectrum inversion
Bibliographic record
Abstract
SUMMAR YIn controlled-source electromagnetic measurements in the near zone or at low frequencies, the real (in-phase) frequency-domain component is dominated by the primary ®eld.However, it is the imaginary (quadrature) component that contains the signal related to a target deeper than the source±receiver separation.In practice, it is dif®cult to measure the imaginary component because of the dominance of the primary ®eld.In contrast, data acquired in the time domain are more sensitive to the deeper target owing to the absence of the primary ®eld.To estimate the frequency-domain responses reliably from the time-domain data, we have developed a Fourier transform algorithm using a least-squares inversion with a smoothness constraint (smooth spectrum inversion).In implementing the smoothness constraint as a priori information, we estimate the frequency response by maximizing the a posteriori distribution based on Bayes' rule.The adjustment of the weighting between the data mis®t and the smoothness constraint is accomplished by minimizing Akaike's Bayesian Information Criterion (ABIC).Tests of the algorithm on synthetic and ®eld data for the long-offset transient electromagnetic method provide reasonable results.The algorithm can handle time-domain data with a wide range of delay times, and is effective for analysing noisy data.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".