Estimation of Q from Zero-offset VSP Data in Gas Hydrate-bearing Zone
Bibliographic record
Abstract
ABSTRACT Q-factor (or Q) that quantifies the attenuation, the intrinsic property of the material, is a very important required factor in extracting useful subsurface material properties such as lithological information, porosity, permeability, viscosity, and the degree of the saturation from the seismic data. When seismic energy propagates through the gas hydrate-bearing zone and a free gas layer below it, the considerable attenuation occurs and affects the amplitude and wavelet shape of recorded seismic data. Thus Q- factor extracted from seismic data can be used to locate the gas hydrate bearing zone and estimate its reserves. The spectral-ratio method has been widely used in computing the frequency-independent Q's from the zero-offset VSP data because of its ease and fastness. We developed a module of the spectral ratio method, and applied it to the synthetic zero-offset VSP data set and field zero-offset VSP data set. The field data were acquired at Mallik 3L-38 gas hydrate research well in Canada. The Q-factors calculated from the synthetic zero-offset VSP data using the spectral ratio method approached closer to the true values for the medium with low Q-factor than high Q-factor. The changes in the Q-factors extracted from the Mallik zero-offset data using the spectral ratio method agreed well with the boundaries of the layers, including gas hydrate zone, depicted in a reflection image.
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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 | 0.000 |
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".