Analysis of velocity dispersion using full‐waveform multichannel sonic logging data: A case study
Bibliographic record
Abstract
ABSTRACT Seismic attenuation and velocity dispersion are potentially able to reveal the rock physical properties of the subsurface. Conventionally, a frequency‐independent quality factor ( Q ) is measured. This Q is equivalent to the total velocity dispersion in a seismic record and is inadequate for analysing the attenuation mechanism or rock physical properties. Here a new method is proposed to extract the velocity dispersion curves so that more attributes can be obtained from full‐waveform multichannel sonic logging data, especially the critical frequency ( f c ) if it is within the bandwidth of the data. This method first decomposes the seismic data into a series of frequency components, computes the semblance of each frequency component for different velocity values, cross‐correlates the semblance matrices of adjacent frequency components to get the velocity gradients, and finally integrates to obtain a velocity dispersion curve. Results of this method are of satisfactory accuracy and robustness. This method is applied to the data acquired in Mallik 5L‐38 gas hydrate research well in Mackenzie Delta, Northwest Territories, Canada. The observed P‐wave velocity dispersion compares well with the geological setting. In the gas hydrate zone (about 900 m–1100 m), high concentration of gas hydrate causes very strong velocity dispersion and a distinct f c at about 15 kHz, likely due to strong scattering of centimetre‐scale inclusions of gas hydrate; concurrently, water flow in connected cracks in some ranges of this zone adds a large part of velocity dispersion and a dimmer f c at about 9.5 kHz. Immediate underneath the gas hydrate zone, abundant free water in weakly laminated sediments causes quite strong velocity dispersion and an f c at about 6.5 kHz. Velocity dispersion is mild and without an obvious f c in sediments above the gas hydrate zone.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".