Development of ice profiler sonar (IPS) target sonar with a logarithmic detector
Bibliographic record
Abstract
Upward-looking sonar (ULS) instruments have become the primary source of data for high resolution and long duration measurements of sea ice drafts to support engineering requirements for oil and gas exploration projects in Arctic and other ice-infested areas. ULS instruments, in the form of ASL's Ice Profiler Sonar (IPS), provide accurate measurements for ice draft on a continuous year-long basis and allow detailed characterization of keel shapes and other ice features. The IPS instrument was originally developed in the 1990's and it was last upgraded by ASL Environmental Sciences Inc. in 2007-2008 through improved instrument design based on more capable microprocessors and more advanced on-board firmware. Another upgrade of the IPS instrument platform is presently underway with the design, testing and implementation of a logarithmic detector module in place of the previously used linear detector module which has been used for the past decade in the instrument. The linear detector module involves the use of an echo sounder detector which generates an analog voltage output from the raw transducer input supplied which is constant, i.e. independent of the time elapsed since the acoustic pulse was originally emitted. While this approach has proven reasonably serviceable, it has the disadvantage that the dynamic range of the instrument is curtailed from the alternative approach of using a logarithmic detector module which has previously been implemented in other ASL upward looking sonar instruments. The larger dynamic range of the log detector avoids using approximate TVG compensation. With the logarithmic sonar detector, the use of discrete threshold values for target detection is avoided and the resulting target detection capability is more robust. The project involved three principal components: (a) construction of a prototype 420 kHz log sonar card; (b) simulations of the response of the IPS log sonar instrument from previous IPS data sets which guided the development of operating firmware; and (c) assembly and field testing of a prototype IPS log sonar unit operated simultaneously with a standard IPS5. The simulations of the IPS5 log sonar outputs derived from previous standard IPS5 data indicate that there are occasional differences in the target detection for borderline cases, but they will not be significant. After iterations to improve the robustness of the target detection algorithm, development of the remaining functions of the IPS5 operating firmware was then carried out and further tested. Finally the prototype IPS5 log sonar instrument unit, along with a standard IPS5 instrument, was field tested in a deep open water environment (to 200 m water depth) in order to test the accuracy of the acoustic range of the sonar targets.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".