Depth image focusing in traveltime map-based wide-angle migration
Bibliographic record
Abstract
Abstract Wide-aperture, prestack depth migration requires application of challenging and time-consuming velocity analysis and depth focusing, collectively referred to here as depth focusing. We present an approach to depth focusing (1) using a detailed starting velocity model obtained by a 1-D transformation of the first-arrival times, followed iteratively by (2) interactive analysis of the common-image gathers, (3) computation of coherency attributes of the wavefield in the depth domain, and (4) 2-D traveltime tomography to update the background velocity model. We employ two interactive method of migration velocities refinement. In the first method (similar to the common-midpoint velocity spectrum approach), by picking the residual, velocity updates directly from the common-image gathers. In another method (analogous to the common velocity stacks), we pick the velocity updates from the areas of maximum coherency in depth sections that are migrated using rescaled traveltime maps. Both types of migration velocity picks, optionally combined with the first arrivals, are inputs for a 2-D traveltime inversion scheme that uses either the infinite-frequency or a finite-bandwidth approximation. This flexible and versatile depth focusing approach is implemented for several prestack depth migration algorithms and illustrated on an application to a real, ultrashallow seismic data set. The technique resolves overburden velocity variations and facilitates reliable high-resolution reflection imaging of a paleochannel that was the target of the study.
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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 | 0.001 |
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 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".