Bibliographic record
Abstract
ABSTRACT Seismic migration is a multichannel process, in which some of the properties depend on various grid spacings. First, there is the acquisition grid, which actually consists of two grids: a grid of source locations and, for each source location, a grid of receiver locations. In addition, there is a third grid, the migration grid, whose spacings also affect properties of the migration. Sampling theory imposes restrictions on migration, limiting the frequency content that can be migrated reliably given the grid spacings. The presence of three grids complicates the application of sampling theory except in unusual situations (e.g., the isolated migration of a single shot record). I analyzed the effects of the grids on different types of migration (Kirchhoff, wavefield extrapolation migration, and slant-stack migration), specifically in the context of migration operator antialiasing. I evaluated general antialiasing criteria for the different types of migration; my examples placed particular emphasis on one style of data acquisition, orthogonal source and receiver lines, which is commonly used on land and which presents particular challenges for the analysis. It is known that migration artifacts caused by inadequate antialiasing can interfere with velocity and amplitude analyses. I found, in addition, that even migrations with adequate antialiasing protection can have the side effect of inaccurate amplitudes resulting from a given acquisition, and I tested how this effect can be compensated.
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 | 0.002 | 0.024 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".