Least-squares reverse time migration with shifted total variation regularization
Bibliographic record
Abstract
ABSTRACT Reverse time migration (RTM), as a state-of-the-art imaging technique, provides outstanding imaging capabilities due to its use of a full wave equation. Least-squares RTM (LSRTM) seeks the solution of a linearized wave equation via the minimization of a data misfit term; however, the quality of the results decreases when the assumptions of the method are not satisfied. This occurs, for example, when we use an erroneous velocity model or inadequate physics for inverting the data. In such cases, appropriate regularization is required to mitigate these shortcomings and stabilize the LSRTM solution. However, even for structurally simple earth models, the reflectivity images are complicated and may not be explained properly by particular regularization methods such as the Tikhonov, total variation (TV), or sparse regularization. Reflectivity images can be thought of as the difference between two structurally simpler components: a piecewise-constant component (the true squared slowness) and a smooth component (the background model). We have used a combined Tikhonov-TV regularizer to regularize these components separately, leading to an effective regularization for the reflectivity image. Because the background model is known in advance, this combined regularization reduces to a shifted TV regularization for which the associated optimization problem is solved efficiently using a new implementation of the Bregmanized operator splitting algorithm applied to the shifted TV method and the usual TV method. We determine the performance of our method with a set of numerical examples. The results confirm that our shifted regularization increases the robustness of LSRTM and allows us to estimate high-quality reflectivity images and properly update the background velocity model.
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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) | 0.001 | 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 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".