Multidimensional inverse-scattering series internal multiple prediction in the coupled plane-wave domain
Bibliographic record
Abstract
ABSTRACT The inverse-scattering series internal multiple prediction and attenuation algorithm predicts multiples using certain combinations of input seismic reflection data events, which are computed in the wavenumber/pseudodepth or plane-wave/vertical traveltime (i.e., τ-p) domains. Significant differences can arise in the algorithms’ output and computational expense depending on which domain is used. Many of these are traceable to the response of the algorithm to the users’ choice of the search-limiting parameter ε. The question of which domain is optimal can be addressed with benchmark synthetics. The compactness of the input to the plane-wave domain algorithm leads to the expectation that it will have a reduced computational expense. Also, the lack of increase in the dominant period (i.e., the “width”) of input events as the horizontal slowness increases leads to the expectation that it will respond well to a constant ε. Both of these expectations are borne out with a 1.5D benchmark example. A 2D plane-wave prediction requires the data to be transformed to the τ-pg-ps, or coupled plane-wave, domain, involving source- and receiver-side horizontal slownesses. An implementation of this transform leads to the first numerical examples of full 2D inverse series τ-p prediction. The arrival times, relative amplitudes, and moveout patterns of multiples from dipping horizons are seen in a benchmark synthetic example to be faithfully determined in the plane-wave formulation; waveform mismatches are, however, observed, which are traceable to the numerics of the forward and inverse transforms. High-resolution Radon transforms are a good candidate to improve the match.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".