Oceanwater Temperature and Salinity from Marine Seismic Data for Weather and Climate Research, the Current State, and Future Possibilities
Bibliographic record
Abstract
Weather prediction and climate modeling requires studying the interactions between the atmosphere and the hydrosphere. Oceanwater temperature and salinity are the two key hydrosphere parameters needed for this study. Oceanic temperature and salinity are measured using eXpendable BathyThermograph and Conductivity-Temperature-Depth sensors, typically deployed at tens to hundreds of kilometers apart, and these sparsely measured data are interpolated in-between as the estimate of the oceanwater temperature and salinity distributions. Due to sparse deployments, the lateral resolutions of these estimates are not high. Marine seismic data on the other hand have a lateral resolution of 25 meters or less, which show visible reflections within the water columns. Amplitude variations of these reflections are due to subtle variations of the oceanwater sound-speed. These sound-speed variations, in turn, are related to the oceanwater temperature, salinity, and pressure via an equation of state. Consequently, there have been several attempts in the recent past to accurately estimate sound-speed from marine seismic data from seismic inversion and then relate it to the temperature and salinity. These seismic inversion methods, however, require estimating an initial sound-speed model, which can be computationally demanding, subject to human errors and biases, and therefore not easy to implement. In this work, we outline the current state-of-the-art on estimating the temperature and salinity from seismic data and describe an automated approach to initial model generation for seismic inversion, which is neither computationally demanding nor subject to human errors and biases. By using a two-step approach of first estimating subtle sound-speed variations from seismic inversion and then estimating the temperature and salinity, we use real seismic data to demonstrate the applicability of this automated method. Furthermore, we also discuss some future possibilities of directly estimating the temperature and salinity from marine seismic data using machine learning.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".