Imaging the northeast lobe of the Sudbury Structure through 2D and 2.5D visco-acoustic full-waveform inversion
Bibliographic record
Abstract
We conduct two-dimensional (2D) and two-and-one-half dimensional (2.5D) visco-acoustic full-waveform inversion in the frequency domain using new long-offset data from the northeast lobe of the Sudbury structure acquired in 2017. We implement a multiscale inversion strategy based on frequency continuation, and the progressive inclusion of later arrivals, in an effort to mitigate the nonlinearity of the inverse problem. This strategy is equally implemented in both 2D and 2.5D schemes, enabling proper comparisons between their respective results. We start by minimizing logarithmic phase-only residuals, and continue with the minimization of conventional phase-amplitude residuals at later stages, addressing large dynamic variations within our dataset. We demonstrate that the 2.5D modeling technique, which requires more computational resources than its 2D counterpart, is not necessary at this location because the acquisition geometry is only mildly crooked. We illustrate this by analyzing inverted source signatures, time-domain synthetic waveforms, and by performing visual comparisons of the inverted 2D and 2.5D velocity models. We successfully retrieve the velocity structure in the first 1.5 km of the subsurface, and the internal stratigraphic character of the Sudbury Igneous Complex (SIC) is identified within this velocity model. Different velocity domains within our model closely correlate with known geology. This allows us to proceed with a joint analysis of the inverted velocity model and the migrated seismic section of the reflection survey that reveals important structural characteristics of prominent SIC layers, such as their inclination degree and thicknesses, as well as their continuation at depth.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".