n-th root entropy functions for blind deconvolution
Bibliographic record
Abstract
Summary Minimum Entropy Deconvolution (MED) seeks to estimate a reflectivity series that consists of a small number of large spikes that can honor the seismic trace. We investigate a method to recover the reflectivity series based on n th -root entropy functions. This approach recovers comparatively small normalized reflectivity values and therefore, it attempts to diminish one of the shortcoming of the MED method. Synthetic data are used to test this family of entropy functions. Introduction MED is a deconvolution method proposed by Wiggins in 1978. The MED method belongs to the category of blind deconvolution methods as it attempts to recover simultaneously the seismic source wavelet and the seismic reflectivity. MED was an important attempt to bypass the classical minimum phase assumption made by spiking and predictive deconvolution techniques (Robinson and Treitel, 1980). Unfortunately, the MED technique tends to retrieve reflectivity sequences that are too sparse and therefore, it produces results with an unrealistic seismic character. One can propose a MED algorithm by maximizing a generalized entropy norm (Sacchi et al., 1994). A particular solution of the generalized entropy norm that depends on a particular choice of the entropy function was proposed by Wiggins (1978). However, there are different ways to define entropy norms by using different entropy functions. For example, logarithmic, quadratic and cubic entropy functions can be defined. The main problem with the entropy norms proposed so far is that they do not recover small amplitudes and they distort the relative amplitude of the reflection coefficients. In this work we present an n
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".