Blind Deconvolution with Toeplitz-structured Sparse Total Least Squares Algorithm
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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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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it