Multiresolution modeling and seismic wavefield reconstruction in attenuating media
Bibliographic record
Abstract
Abstract The propagation of the seismic wavefield through a viscoelastic medium is a multiresolution process, in which depth of propagation and scale are closely associated with one another. We propose the multiresolution wavefield reconstruction (MRWR) as a means to directly integrate such concepts of scale into the backpropagation component of an imaging method. MRWR produces a reconstruction of the wavefield (at some fixed depth d), in which each scale term reconstitutes the resolution that was lost as the wavefield propagated some step-length towards the measurement surface, away from d. Concurrently, MRWR provides a stable platform for this removal of the effects of absorption in propagation. In a multiresolution model of propagation, the viscoelastic propagation kernel is seen, mathematically, to fill the role of the scale function in multiresolution theory, as it operates on a wavefield to propagate it through some distance. The suppression of high-resolution components of the wavefield via this scaling/propagation kernel function is readily illustrated with a simple numerical example. We use this scale-based view of propagation and the differential inversion method to provide a physical and mathematical rationale for MRWR. Two cases of the MRWR formula are derived, and applications in one dimension and two dimensions, for synthetic data, and one field data example, are presented to demonstrate its use.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".