Ambient-noise inversion in ocean geoacoustics and seismic-hazard assessment
Bibliographic record
Abstract
This paper presents methodologies and results for the estimation of seabed and soil-column geophysical properties based on Bayesian inversion of ambient ocean-acoustic and seismic noise, respectively. In both marine and terrestrial applications, beamforming is applied to array-based measurements to select the direction of arrival of ambient-noise energy. For the ocean-acoustic application, wave-generated surface noise is recorded at a vertical line array and beamformed to extract up- and down-going energy fluxes from which bottom loss vs. angle can be computed and inverted for geoacoustic profiles. Results from ambient-noise measurements at the Malta Plateau are presented and compared to controlled-source inversions and core measurements. The terrestrial seismic application is aimed at earthquake-hazard site assessment, which requires knowledge of the shear-wave velocity profile over the upper tens of meters of the soil column. In this case, urban seismic noise is recorded on a geophone array and beamformed to determine the dominant direction of arrival over short time windows, from which the Rayleigh-wave dispersion curve can be estimated and inverted. Two sites with differing geology are considered, and results are compared to invasive (borehole) measurements.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".