Application of Surface-wave modeling and inversion in Cordova Embayment of northeastern British Columbia
Bibliographic record
Abstract
Summary With increasing activities in shale plays, surface-wave analysis and inversion is finding growing applications in the hydrocarbon exploration industry. This is, perhaps, an acknowledgement that nearsurface velocity anomalies play a critical role in imaging the deeper heterogeneous shale reservoirs. The spatial and vertical heterogeneities, seen in the shale reservoir must be corrected for any nearsurface velocity effects, through proper statics correction or through imaging with a velocity model that properly takes account of the near-surface heterogeneities. Near-surface velocity anomalies are quite severe in the Cordova Embayment of northeastern British Columbia, where the Mid-Late Devonian shale are targets. Analysis and inversion of dispersive surface-waves appearing in the form of ground roll in land data is an appropriate tool for this purpose. One main goal is to estimate shear-wave statics, so mode-converted shear-wave data can be used in shale-reservoir characterization. We applied a newly developed surface-wave analysis, modeling, and inversion method (SWAMI) to obtain near-surface velocity heterogeneities in the Cordova Embayment area. Pulse wave data on 2D test lines containing frequencies as low as 1 Hz were used for this purpose. The use of pulse data was challenging; but low-frequency contents in data helped model shear velocity to 100 m or more below surface. The inversion results were validated through inversion of synthetic Rayleigh-wave data generated with an appropriate and industry-standard elastic modeling program. The work with test lines helped develop strategies for additional surveys for building a detailed near-surface velocity model.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".