On acoustic waveform tomography of wide-angle OBS data--strategies for pre-conditioning and inversion
Bibliographic record
Abstract
We successfully apply the acoustic Laplace–Fourier waveform tomography method to delineate P-wave velocity structures of the mega-splay fault system in the central part of the seismogenic Nankai subduction zone offshore Japan, using densely sampled wide-angle ocean bottom seismograph (OBS) data originally acquired in 2004. Our success is due to new and carefully designed data preconditioning and inversion strategies to mitigate (i) the well-known non-linearity of waveform inversion, (ii) the challenges arising from crustal-scale survey designs (e.g. undersampling of the OBSs), and (iii) modelling errors due to the use of the acoustic assumption. We identify a sixfold set of key components that together lead to the success of the high-resolution waveform tomography image: (i) Availability of low-frequency components (starting at 2.25 Hz) reducing the non-linearity, and access to large offset data (up to 55 km) increasing the depth of illumination and the recovery of low wavenumber components. (ii) A highly accurate traveltime tomography result (with an rms error of approximately 60 ms) that further mitigates the non-linearity. (iii) A hierarchical inversion approach in which phase spectra are inverted first to reduce artefacts from the acoustic assumption, and amplitude information is only incorporated in the final stages. (iv) A Laplace–Fourier domain approach that facilitates a multiscale approach to mitigate non-linearity by restricting the inversion to the low frequency components and early arrivals first, and sequentially including higher frequencies and later arrivals. (v) A pre-conditioning strategy for eliminating undesirable high wavenumber components from the the gradient. (vi) A strategy for source estimation that reduce the influence of the instrumental design. In the OBS case study used for illustration purposes, Laplace–Fourier waveform tomography retrieves velocity anomalies as small as 700 m (horizontally) and 350 m (vertically) above the top of the Philippine Sea Plate. The resulting velocity structures include low-velocity zones and thrust structures which have not been previously identified clearly. The velocity models are validated by scrutiny of synthetic and observed waveforms, by evaluating the coherency of source estimates, and by comparison with 3-D pre-stack migrated (PreSDM) images. Chequerboard tests and point-scatter tests demonstrate both the reliability and the limitations of the acoustic implementation.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".