Near-surface scattered waves enhancement with source-receiver interferometry
Bibliographic record
Abstract
ABSTRACT Near-surface scattered waves (NSWs) are the main noise in seismic data in areas with a complex near surface and can be divided into surface-to-surface scattered waves and body-to-surface scattered waves. We have developed a method for NSW enhancement that uses modified source-receiver interferometry. The method consists of two parts. First, deconvolutional intersource interferometry is used to cancel the common raypath of seismic waves from a near-surface scatterer to the common receiver and the receiver function. Second, convolutional interreceiver interferometry is used to compensate the common raypath of seismic waves from the common source to the near-surface scatterer and the source function. For an isotropic point scatterer near the earth’s surface in modified source-receiver interferometry, a body-to-surface scattered wave can be reconstructed by constructive interference not only among three body-to-surface scattered waves but also among a body-to-surface scattered wave and two surface-to-surface scattered waves; a surface-to-surface scattered wave can be reconstructed by constructive interference not only among three surface-to-surface scattered waves but also among a surface-to-surface scattered wave and two body-to-surface scattered waves. According to stationary phase analysis based on the superposition principle, we have developed a so-called dual-wheel driving configuration of modified source-receiver interferometry for enhancing NSWs in the data of conventional seismic exploration. The main advantages of the scheme are that (1) it can be used to enhance NSWs without the need for any a priori knowledge of topography and near-surface velocity, (2) it can be used to reconstruct NSWs from real sources to real receivers, including 3D near-surface side-scattered waves, and (3) it can be applied to conventional seismic data with finite-frequency bandwidth, spatially limited and sparse arrays, different source and receiver functions, and static correction. Numerically simulated data and field seismic data are used to demonstrate the feasibility and effectiveness of the scheme.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".