3D generalized spherical multifocusing seismic imaging
Bibliographic record
Abstract
ABSTRACT We introduce a 3D generalized spherical multifocusing (GSMF) algorithm to generate a high-resolution 3D stacked volume that is equivalent to a synthesized 3D zero-offset wavefield for crooked-line/3D seismic data. The proposed algorithm can be applied to arbitrary recording geometry from areas with irregular topography, a complex near the surface, and complex subsurface. The 3D GSMF method simultaneously corrects for elevation statics, nonhyperbolic moveout associated with reflections beneath complex overburden structures, and azimuth-dependent dip-moveout effects. In addition, the formulation is dually generalized for the optical domain and the effective medium. The optical domain and effective medium parameterizations account for heterogeneity either by shifting the reference time to project the problem into the optical image space or by adjusting the velocity of an effective overburden, respectively. We test the performance of our method using 3D synthetic data with 3D and crooked-line surveys. The numerical tests have shown that the accuracy of the new approximation is significant for gently to highly curved interfaces beneath low to relatively high heterogeneous overburden with rugged topography, even at large offsets and midpoint separations. In addition, we rigorously evaluate the method using 3D real seismic data acquired over a complex thrust-belt area with rugged terrain. Compared with conventional 3D stacking, the new formulation yields a high resolution and accurate seismic stacked volume from land seismic data collected with arbitrary 3D geometries.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".