High-order pseudo-random binary signals for frequency domain electromagnetic explorations
Bibliographic record
Abstract
ABSTRACT In the field of frequency-domain electromagnetic (EM) exploration with artificial sources, various signals were employed. However, these signals often failed to encompass all relevant frequencies of interest (or effective frequencies), necessitated signal replacements during operations, which significantly affected fieldwork efficiency. To address this issue, a novel method for generating pseudo-random binary signals was proposed, which included a wide range of frequency components. With this method, the frequency requirements for most frequency-domain EM exploration projects were met using only one waveform that is specially designed for the project. Consequently, the need for signal replacement during operations was eliminated, leading to a great improvement in the efficiency of field work. The generation method was based on waveform superposition and hard clipping, combined with unit integration. To improve the efficiency of signal generation, we adopted a specifically modified multi-objective particle swarm optimization algorithm. To ensure the practical applicability of the generated signals, the optimization was designed to target two key properties: energy concentration and uniformity of the effective frequency spectrum. This approach enabled the rapid generation of signals that met engineering requirements in only a few minutes. This type of signal has been successfully applied in multiple field exploration projects, and has delivered excellent results, verifying its effectiveness in improving both work efficiency and anti-interference capability.
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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 | 0.001 |
| 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) | 0.001 | 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".