Multidimensional signature deconvolution and free-surface multiple elimination of marine multicomponent ocean-bottom seismic data
Bibliographic record
Abstract
Abstract This paper presents a wave-equation method for multidimensional signature deconvolution (designature) and elimination of free-surface related multiples (demultiple) in four-component (4C) ocean-bottom seismic data. The designature/demultiple method has the following characteristics: it preserves primary amplitudes while attenuating free-surface related multiples; it requires no knowledge of the sea floor-parameters and the subsurface; it requires information only of the local density and acoustic wave propagation velocity just above the sea floor; it accommodates source arrays; and no information (except location) of the physical source array, its volume, and its radiation characteristics (wavelet) is required. Designature is an implicit part of the demultiple process; hence, the method is capable of transforming recorded reflection data excited by any source array below the sea surface into free-surface demultipled data that would be recorded from a point source with any desired signature. In addition, the incident wavefield is not subtracted from the data prior to free-surface demultiple; hence, separation of incident and scattered fields is not an issue as it is for most other free-surface demultiple schemes. The designature/demultiple algorithm can be divided into two major computational steps. First, a multidimensional deconvolution operator, inversely proportional to the time derivative of the downgoing part of the normal component of the particle velocity just above the sea floor, is computed. Second, an integral equation is solved to find any component of the designatured, free-surface demultipled multicomponent field. When the geology is horizontally layered, the designature and free-surface demultiple scheme greatly simplifies and lends itself toward implementation in the τ–p domain or frequency–wavenumber domain as deterministic deconvolution of common shot gathers (or common receiver gathers when source array variations are negligible).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".