Enhancing the Near-Surface Image Using Duplex-Wave Reverse Time Migration
Bibliographic record
Abstract
Abstract Reverse time migration (RTM) involves zero-lag cross-correlation of forward extrapolated source function wavefields and backward extrapolated receiver wavefields. For a near surface with complex structures and velocity anomalies, forward propagating the source wavelet generates wavefields containing reflections, near-surface multiples, and scattered direct arrivals. The wavefields are recorded as upgoing arrivals contaminated by the same reflections, near-surface multiples, and scattered signals, which can be critical for imaging near-surface structures and scatterers. Here, we develop a new depth migration, duplex reverse time migration (DRTM) technique to improve imaging of complex near-surface structures. DRTM uses the direct arrival as a source to forward propagate and generate source wavefields, and reversely extrapolated recorded data in a zero-lag cross-correlation imaging condition to generate the final section. The interaction between the data components during cross- correlation can use primaries and multiples to image the near-surface structure correctly. Cross-talk artifacts may exist, but they are comparatively weak. DRTM is demonstrated on both synthetic and field data examples showing an enhanced image in areas with complex near-surface structures compared to conventional RTM imaging methods. The new algorithm can significantly enhance shallow imaging without additional computation costs compared with conventional RTM. It can produce an image with higher resolution and signal-to-noise (S/N) ratio by replacing the source wavelet with the recorded direct arrivals, which include near-surface information necessary to boost the image in areas with near-surface complexity. Since the direct arrivals are one of the most energetic events recorded, the resultant image is typically of high S/N. The wave can also illuminate shallow zones better than primaries in marine environments.
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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) | 0.001 | 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 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".