Blind Deconvolution with Toeplitz-structured Sparse Total Least Squares Algorithm
Bibliographic record
Abstract
Summary Blind deconvolution simultaneously solves for the reflectivity series and the wavelet given the noise corrupted seismic recordings. This is an ill-posed problem and difficult to solve. Developing a reliable single channel blind deconvolution technique is an ongoing research. Here, we formulated the blind deconvolution as a fully perturbed linear regression model and developed an efficient iterative algorithm based on Total least squares (TLS) method. Unfortunately, TLS method, with or without regularization, does not provide consistent estimators for the under-determined linear system of equations. To remedy this shortcoming, we added more constraints into the equations. We assume that the reflectivity series is sparse and moreover, to reduce the model space and the number of unknowns, the algorithm preserves the Toeplitz structure of the data matrix. In addition, there is no assumption about the phase of the wavelet. The developed algorithm is an alternating minimization method and can be used for different applications such as blind deconvolution, perturbed compressive sensing and dictionary learning. In this paper, we only focused on blind deconvolution. The performance of the algorithm is evaluated on synthetic and real datasets. Real data examples are belonging to lines A and D of the Teapot Dome seismic survey.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".