High resolution least‐squares wave equation AVA imaging: Feasibility study with a data set from the Western Canadian Sedimentary Basin
Bibliographic record
Abstract
This paper presents a regularized least-squares pre-stack 3-D wave equation Amplitude versus Angle (AVA) migration algorithm and explores the feasibility of this class of methods to process field data. We pose seismic imaging as a linear inverse problem that incorporates weighting matrices in model and data space. The goal is to remove additive noise and artifacts that arise from data acquisition, operator mismatch and additive coherent and incoherent noise. We solve the inverse problem with the conjugate gradients method and, in addition, we accelerate the convergence of the CG scheme by a preconditioning strategy. We have applied the regularized least-squares migration (RLSM) algorithm to a 3-D data set from the Western Canadian Sedimentary Basin. The inversion significantly improves the quality of the common image gathers. The accuracy of our algorithm is confirmed by a detailed comparison of inverted and synthetic CIGs. We also observe an substantial enhancement of vertical resolution as a consequence of improving the coherence of the inverted common image gathers and an implicit deconvolution that is embedded in the method.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".