Quality factor estimation with continuous wavelet transform from the true amplitude reversetime migrated image gather: An example from the Cascadia subduction zone
Bibliographic record
Abstract
The Cascadia subduction zone poses major geohazards to the northwestern United States and Western Canada. Within the accretionary prism in this zone, there are high concentrations of methane hydrates with clear observational records of continuous methane seepage into the ocean. Quantifying this seepage is vital not only for the overall fluid budget of the subduction zone, but also for its impacts to the climate in the event of a major earthquake. In addition, these methane hydrates can be exploited as a potential source of energy. Earlier work on characterizing the methane hydrates from seismic data indicated relatively low attenuation above and high attenuation below the hydrate layer in the Cascadia ocean margin. Seismic attenuation, given as the quality factor, is an indicative of permeability and fluid content and thus vital to quantifying the dynamic behavior of the methane-water system at the Cascadia ocean margin. In this work, we developed a method to estimate quality factor from seismic data using continuous wavelet transform. Applying the method on the true amplitude reverse-time migrated image gathers on real seismic data from the Cascadia subduction zone, we show that our estimates agree with the previous studies in the area. Estimated quality factors from this method will be vital to generation of the initial model for joint prestack waveform inversion and reverse-time migration workflow, in which the visco-elastic earth model and depth image are simultaneously obtained through an iterative updating procedure. P- and S-wave quality factors (QP and QS), estimated from this workflow could then be used to estimate the permeability and fluid saturation for a detailed description of the dynamic behavior of the accretionary prism methane hydrates at the Cascadia ocean margin. Presentation Date: Wednesday, September 18, 2019 Session Start Time: 9:20 AM Presentation Start Time: 11:00 AM Location: Poster Station 5 Presentation Type: Poster
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".