Dynamic motion residuals in swath sonar data: Ironing out the creases
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
As the component sensors in swath sonar systems have improved, the focus on total system performance has turned increasingly to the remaining imperfections in the system integration. Of particular concern is. that faint but systematic across track ribbing often remains in otherwise high-quality data. Traditional field calibration procedures primarily look for the signature of static systematic error contributions. These procedures (the conventional patch test) only examine a subset of the pos sible systematic biases in the configu ration of an integrated swath sonar sys tem. Other systematic biases can cause dynamic rather than static signa tures in the resulting bathymetric data. These dynamic errors can be separat ed into those that produce errors that vary with periods in the ocean wave spectrum (most commonly referred to as the ‘wobbles’) and those whose period is dictated by the vessel's long period accelerations (turns and other course changes, obstacle avoidance and speed changes). Herein the theory behind the cause for a number of common wobble sources is examined. For the case of shallow water surveys, where the ping period is Figure 1: sun-illuminated terrain models of EM1002 bathymetric data in 30m of water. The top image shows data as originally collected with pronounced ship-track orthogonal ribbing. The bottom plot shows data after shifting the motion time series by-20ms. The peak to peak magnitude of the apparent rippling is on the order of +/-1.0-1.5 per cent (well within the required standard- IHO order 2). Data courtesy of the Geological Survey of Israel short with respect to the typical wave period, the wobble signatures can be easily discerned. The dif ferences in the signatures of each of the wobbles are highlighted allowing rapid classification and thus a means of removal of the underlying system atic bias.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.003 | 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 it