Angle-domain least-squares reverse time migration through full-wave and ray-theory Hessian operators
Bibliographic record
Abstract
ABSTRACT Least-squares reverse time migration (LSRTM) is a powerful method for imaging complex subsurface structures. Traditional LSRTM implementations typically recovered a stacked image interpreted as a velocity perturbation, which lacked a physical interpretation when analyzing angle-dependent responses. An angle-domain LSRTM framework was introduced to invert for angle-dependent reflectivity using full-wave and ray-theory Hessian operators. The method enabled a computationally efficient strategy for LSRTM by assuming that the angle-domain migration Hessian operator was a diagonally dominant banded matrix. The migration step followed a standard common-shot implementation of angle-domain reverse time migration, whereas the Hessian operator was explicitly computed using wave-equation-based amplitudes and traveltimes. Although the method required additional finite-difference simulations at receiver positions, it remained computationally efficient and yielded amplitude-preserving angle-dependent reflectivity images. Numerical experiments using both synthetic and field data were conducted to test the proposed algorithm, demonstrating the fidelity of amplitude variation with angle responses. Furthermore, our analysis demonstrated that the ray-theory angle-domain Hessian operator provided a physically justified, computationally efficient, and memory-efficient alternative to the full-wave operator for modeling and inversion.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".