Bibliographic record
Abstract
ABSTRACT Inverse-Q filtering is an important seismic-processing operation often used to correct for attenuation and dispersion effects and increase the resolution of reflection records. However, it is important to realize that the Q is an apparent (phenomenological) attribute of the propagating wavefield and not guaranteed to be a material property. By recognizing the apparent character of the Q, the attenuation-correction procedure can be significantly extended and generalized. Our approach consists of forward modeling the propagating source waveform by using multiple physical laws followed by multiple types of inverse filtering. The modeling and inverse-filtering algorithms are selectable according to the geology of the study area, data, and goals of processing, which may include reduction of attenuation effects or more general enhancements of reflectivity images. Apparent Q models are inherently smooth in space, which facilitates efficient use of time-variant deconvolution implemented by using overlapping tapered time windows. When using conventional Q models and frequency-domain deconvolution, this procedure contains all existing types of inverse-Q filtering. However, many more realistic forward modeling approaches can (and should) be used depending on the specific subsurface environments, such as wavefront focusing and defocusing, scattering, solid viscosity, or internal friction caused by pore-fluid flows. In general, velocity-dispersion relations cannot be inferred from the frequency-dependent Q and need to be considered separately. It is more precise to view frequency-dependent velocity dispersion and Q as concomitant and arising from a common physical mechanism of wave propagation. Time-domain deconvolution, such as an iterative method well-known in earthquake seismology, offers significant improvements in attenuation-corrected images. The approaches are illustrated on a real reflection data set by using several attenuation laws and types of deconvolution.
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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) | 0.000 | 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 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".