Tomographic inversion of geoacoustic properties in a range-dependent shallow-water environment
Bibliographic record
Abstract
This paper presents a matched-field tomographic method to estimate the geoacoustic properties of the ocean bottom for a range-dependent medium in shallow water. The inversion method has been developed in order to interpret experimental data from the Haro Strait PRIMER sea trial. This experiment was carried out in June '96 and used low-frequency broadband signals that were received on three vertical line arrays. Inversion of the data is particularly difficult because of the complex bathymetry of the Haro Strait experimental site. For this inversion, a range-dependent ray code was developed to solve the forward problem, allowing an arbitrarily layered bottom environment. The inversion scheme is based on modeling the propagation time and the amplitude of the recorded data, and a simple new cost function is proposed. The signal ray paths are identified automatically using a simple process that compares calculated and measured travel times. Data from multiple source positions are used to invert the range dependence of the geoacoustic model. The environment is separated into segments, and within each segment the inversion is carried out layer by layer for a multilayer geoacoustic model. Starting with the topmost layer, the range-dependent thickness and sound speed are estimated via a Monte Carlo method. Inversion results are presented for synthetic and experimental data from the Haro Strait sea trial.
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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 | 0.000 | 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".