Reducing Spatial Aliasing in Wave-Equation Multiple Attenuation
Bibliographic record
Abstract
Abstract Multiples are a major problem in offshore seismic exploration. Wave-equation methods are a popular tool for multiple attenuation, but behave poorly in the presence of spatial aliasing. This paper will propose a method to reduce the effect of spatial aliasing. It does this by first applying a linear transformation to the wave equation. This transformation reduces dips in the data, but does not involve approximations, nor does it change the form of the equation. Wave extrapolation is then performed using an efficient Kirchhoffstyle phase-shift operation. The modeled multiples are adaptively subtracted from the original traces with a multichannel constrained filter. The proposed method has givengood results on a data set from the East Coast of Canada. Introduction The basic model in seismic processing assumes that reflection data consists of primaries only. If multiples are not removed they can be misinterpreted as, or interfere with, primaries. Therefore the attenuation of multiple reflections has been an important research subject and many methods have been developed. The methods can be classified into two types: those that exploit the periodic nature of multiples, and those that exploit the moveout differences between multiple and primary reflections. The first category includes predictive deconvolution in the x-t or ?-? domain. Predictive deconvolution assumes that the multiples are periodic within an application window and that the primary events are not - that is, the multiples can be predicted and thereby subtracted from the seismic records. In the prestack x-t domain, water bottom multiples are periodic only for zero offset traces and flat-water bottoms. To achieve periodicity of multiples for far-offset traces, transforms such as the ?-? transform or the radial trace method must be applied. Still, for shallow water depths (shorter period multiples), periodicity only holds in a limited window. Predictive deconvolution can introduce spurious events and needs to be applied with care. It is more effective for removing "ringing" multiple energy than longer period waterbottom multiples. The second category includes methods in the F-K, K-L and ?-? domains. While these methods are routinely used and can be very effective, there have a number of problems. All of these methods lose effectiveness when the velocity discrimination between primary events and multiples is small. This is the case at near offsets where there is little or no separation of primary and multiple events, and at all offsets when there is little velocity difference. Their effectiveness is further reduced when the seismic events violate the modeling assumptions (non-hyperbolic events for F-K and K-L, or non-parabolic events after NMO for ?-?). Recently there has been interest in techniques based on wave theory, which can be considered extensions of conventional predictive deconvolution. Multiples are predicted by either wavefield extrapolation1,2,3 or wavefield inversion4. They are then subtracted from the original data.
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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) | 0.002 | 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".