Waveform Tomography Imaging of a Megasplay Fault System in the Seismogenic Nankai Subduction Zone
Bibliographic record
Abstract
We apply frequency-domain acoustic waveform tomography to form quantitative, high-resolution P-wave velocity images of a megasplay fault system within the central Nankai subduction zone offshore of southwest Japan, using controlled source Ocean Bottom Seismometer (OBS) data originally acquired in 2004. The waveform inversion was conducted in two stages: 1) using the phase-only logarithmic objective function, and 2) followed by the phase-and-amplitude logarithmic objective function. The two-stage approach minimizes artifacts due to elastic effects, and enables illumination of deeper parts of the model. The Laplace-Fourier domain approach was also adopted to mitigate non-linearity of waveform tomography by emphasizing the contribution from early parts of waveforms. We handled the difficulties arising from the data acquisition such as sparse OBS intervals and bubble oscillation by an appropriate data preprocessing, source estimation, and wavenumber filtering. The waveform tomography successfully delineated major geological features including the megasplay fault, and associated overpressured low velocity zones. The results yield significant improvements in the data fit to the OBS data, in comparison with those obtained from traveltime tomography. A comparison with the previous migration images not only confirmed the validity of the results, but also highlighted the ability of waveform tomography to fill in the part not imaged by the migration images.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".