Can the sub‐basalt imaging problem be solved by gradient‐based waveform inversion?
Bibliographic record
Abstract
In this paper, we define the characteristics of suitable starting models for gradient-based waveform inversion, when applied to sub-basalt imaging. Although our study is carried out on a 1-D model, our conclusions are also valid for more general cases. Since the inverse problem encounters strong non-linearities, the starting model plays a strong role in guiding the inversion. By analyzing the evolution of the misfit function with respect to the degradation of the true model by smoothing, we develop criteria for a “good ” starting model, i.e. one that will yield convergence to the global, rather than a local minimum. These criteria depend on the lowest available data frequency: the lower this limit is, the more tolerant the algorithm is of missing high wavenumber information. Even given a lower limit of approximately 7 Hz, we conclude that the criteria for waveform of sub-basalt targets will not be met if the starting model is obtained from standard methods. Figure 1: Shot point gather in the true model with a source Ricker centered on 4 Hz. One can identify the water bottom reflection (WBR), the top basalt reflection (TBR) and the bottom basalt reflection (BBR). The free surface multiples are not present in these data.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".