2D waveform tomography applied to long-streamer MCS data from the Scotian Slope
Bibliographic record
Abstract
Abstract Detailed velocity models of the earth's subsurface can be obtained through waveform tomography. The accuracy of the long-wavelength component of such velocity models, which is the background velocity field, is particularly sensitive to modeling low-frequency refracted waves that have long paths through target structures. Thus, field examples primarily have focused on the analysis of long-offset wide-angle data sets collected using autonomous receivers, in which refractions arrive at significantly earlier times than reflections. Modern marine acquisition with long streamers now offers the ability to record refracted waves with high spatial density and uniform source, both in shallow and deep water. We used 2D multichannel seismic (MCS) data acquired with a 9-km-long streamer over the Scotian Slope in water depths of ∼1600 m. The refracted arrivals, although mostly restricted to far-offset receivers, provided sufficient information to successfully invert for a high-resolution background velocity field. Using a frequency-domain acoustic code over frequencies from 8 to 24 Hz on two crossing profiles, we found that the limited refracted waves can constrain the velocity field above the depth of the turning waves (∼1.5 km below seafloor). Several important features were resolved by the waveform velocity model that were not present in the initial traveltime model. In particular, a high-velocity layer at 300 m below the seafloor, interpreted as gas hydrates, was imaged even where a characteristic bottom-simulating reflector was not visible. At 750-m depth, a strong velocity increase of 300 m/s existed beneath a gently dipping reflector along which low-velocity zones, possibly related to gas, were present. Velocity models were highly consistent at the crossing point between the two profiles. The depth extent of the MCS waveform tomography constrained by refractions could be extended by even longer streamers (e.g., 15 km) or by joint inversion with data from ocean-bottom seismographs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".