2.5D Multi-Focusing Imaging of Crooked-Line Seismic Surveys
Bibliographic record
Abstract
Summary Due to logistical and environmental restrictions, seismic data are often acquired with a 2D crooked-line geometry. The crookedness of profiles, irregular topography, and complex subsurface geology with steeply dipping and curved interfaces could negatively affect the signal-to-noise ratio of the data. Crooked-line geometry violates the assumption of a straight survey line that is a basic principle behind the 2D Multi-focusing (MF) method. Irregular survey geometry leads to the cross-profile spread of midpoints in the vicinity of the processing line. In this research, we have developed a novel Multi-focusing algorithm for crooked-line seismic data and revisited its travel-time equation to achieve better signal alignment before stacking. We present a 2.5D Multi-focusing reflection travel-time expression which explicitly takes into account the midpoint dispersion and cross-dip effects. The new formulation corrects normal, in-line, and cross-line dip moveouts simultaneously. The 2.5D Multi-focusing method can perform automatically with a semblance based global optimization search on the real data. We investigated the accuracy of the new formulation by testing on different synthetic models. Numerical tests show that the new formula can focus the primary reflections with good precision at their right location, remove anomalous dip-dependent velocities, and extract true dips from seismic data for structural interpretation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".