Efficient τ-<i>p</i>domain waveform inversion, part 2: Sensitivity to<i>p</i>component setting, source spacing and noise
Bibliographic record
Abstract
Summary Full Waveform Inversion (FWI) has been widely studied in recent years but challenges remain. Issues include computational cost, slow convergence rate, cycle skipping problem and so on. Aiming at these obstacles, we develop the τ-p domain waveform inversion with a assemblage of strategies and present the inversion results with different scaling methods in a companion paper (Pan et al., 2014b). Generally, for per iteration in FWI, slant stacking over a set of p values should be performed to balance the updates. To reduce the computational burden further, we illustrate slant update strategy with varied p values in which the model updates can be balanced as the iteration proceeds. The phase-encoding method in τ-p domain can reduce the computational cost considerably, but unfortunately, it can also involve serious crosstalk artifacts especially for sparsely sampled sources. A further examination of the anti-aliasing rules in the Random transform reveals that the source spacing has a negative relationship with ray parameter spacing. Different ray parameters are responsible to illuminate the subsurface layers with different dip angles. So, in this paper, we analyze the influences of source spacing and ray parameter range on the τ-p domain FWI. In practical application, the presence of noise can increase the ill-posedness of the least-squares inversion problem. Hence, we also analyze the stability of τ-p domain FWI with noise data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".