Mitigation of guided wave contamination in waveform tomography of marine seismic reflection data from southwestern Alaska
Bibliographic record
Abstract
ABSTRACT We have applied 2D frequency-domain acoustic waveform tomography to two different sections of a marine seismic reflection line from southwest Alaska: one section with a deep igneous basement overlain by a thick pile of sediments and the other section with a shallow basement and a thin sedimentary cover. We have evaluated the appearance of dispersive guided waves on both sections, and we have determined that with appropriate data preconditioning it is possible to invert the data using 2D acoustic waveform tomography. Where the basement is deep, we first reduced the dispersive wave contamination of the seismic field data by trace editing, band-pass filtering, and careful choice of the data window for inversion. We then tested different objective functions and inversion scheduling before selecting an approach based on the logarithmic phase, which could be followed by joint phase and amplitude inversion. Where the basement is shallow, the starting model itself, which was generated by ray-based first-arrival tomography, generated acoustic guided waves, necessitating the use of an absorbing boundary condition at the free surface. Logarithmic phase inversion was used, but the amplitude inversion did not converge. To invert seismic data from both sections, we used a layer stripping strategy in which the gradient was used at each stage of the inversion process to check the corresponding model updates. Our results were validated by comparison between synthetic and observed waveforms, comparison of residual phase error plots for the initial and final velocity models, and comparison of waveform tomography velocity models with migrated images. Waveform tomography permits interpretation of the subsurface close to the seafloor where reflection images are contaminated by water-layer multiples, and we inferred the existence of a fault zone from a low-velocity anomaly within the igneous basement.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".