Elastic least-squares one-way wave-equation migration
Bibliographic record
Abstract
Least-squares migration seeks a reflectivity model that fits the observed data. It is used to compensate for acquisition noise, poor sampling of sources and receivers on the surface, as well as poor illumination of the subsurface. To date, least-squares migration has been mainly restricted to the imaging of acoustic wavefields. We have developed an extension of one-way wave-equation least-squares migration for elastic wavefields in isotropic media. Least-squares migration is an iterative method that requires a forward and an adjoint operator. In elastic least-squares one-way wave-equation migration, the forward operator generates data components from multiparameter images by recursive wavefield decomposition, extrapolation, and recomposition. Conversely, the adjoint operator generates multiparameter images from data components by recursively applying the adjoint of the wavefield recomposition, extrapolation, and wavefield decomposition operators. We use an extended imaging condition and regularize the inversion by applying a smoothing filter on the depth-angle axes of each common image point gather to reduce the effect of source/receiver sampling, noise, and crosstalk artifacts. Elastic least-squares migration is able to compensate for irregular subsurface illumination in elastic imaging and provides an alternative approach to interpolation and wavefield separation of multicomponent seismic data.
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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) | 0.000 | 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".