Low-frequency geoacoustic modeling in shallow water sediment environments.
Bibliographic record
Abstract
The goal in geoacoustic modeling is to develop realistic geophysical models of the ocean bottom that can be used in numerical calculations of the acoustic field in the ocean. The ocean bottom is assumed to be a layered structure of different types of sediment material that have been deposited over geological times. However, in shallow water the bottom is generally much more complex. The sediment material is variable on different spatial scales horizontally and is inhomogeneous in depth below the sea floor. Despite this complexity in realistic bottom environments, there has been considerable success using the simplified approach of a layered, range-independent geology in low-frequency (20–500 Hz) applications with inversion techniques that provide estimates of geoacoustic model parameters and their uncertainties. This paper reviews some of the most effective inversion techniques and compares their performance in estimating realistic and effective geoacoustic profiles in applications with data from the recent Shallow Water ‘06 experiments on the New Jersey continental shelf. Conditions are discussed that limit the performance of present day inversion techniques. These include rough interfaces on and below the sea floor, consolidated material that supports shear wave propagation, and range variation of sub-bottom structure.
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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 | 0.001 | 0.001 |
| 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".