Practical Application of Surface-Wave Methods to Estimate a Near-Surface Velocity Model at Spring Coulee
Bibliographic record
Abstract
Abstract Variations in the thicknesses and velocities of near-surface layers can cause serious problems for seismic reflection imaging of the deeper subsurface. Static corrections, calculated from near-surface velocity models, are used to remove the effects of the variable near surface. In this paper, near-surface layers are characterized using a method known as Multi-Channel Analysis of Surface Waves (MASW). This method is mainly used for geotechnical engineering applications and is based on the dispersion properties of Rayleigh-waves. Here, it will be used for calculating S-wave statics for a site located at Spring Coulee, Alberta. The results will be compared to those generated by applying Generalized Linear Inversion (GLI) of first arrival times; a widely used and proven method for obtaining near-surface velocity models for both P- and S-waves. In the case of an isotropic homogeneous half space, Rayleigh-waves are non-dispersive with different frequencies traveling at the same velocity. In a real layered earth, Rayleigh-waves are dispersive (the velocity is dependent on frequency) with multiple modes. The phase velocity of surface waves traveling through a layered earth model is a function of the frequency and four earth parameters; P-wave velocity, S-wave velocity, density, and the layer thickness. The Rayleigh-wave phase velocity is most sensitive to the S-wave velocity and the layer thickness. From an initial model, Rayleigh-wave phase velocities of different frequencies (dispersion curves) are calculated, and then compared to the measured dispersion curves. Then the initial model is updated until some acceptable agreement with the measured dispersion curves is reached. In the Spring Coulee analysis area, the S-wave near-surface models, obtained by the GLI method, had different velocity layering and base of weathering from those of the Pwaves. The MASW S-wave velocity model correlated well to the GLI model. For converted wave PS sections, the improvement in the quality of reflection events after applying S-wave statics from the MASW model is similar to the event quality after application of the S-wave statics from the GLI refraction model. Identifying and picking S-wave first arrivals manually is a difficult and time consuming process. Unfortunately, automatic picking programs do not work well for S-wave refractions. Confusing S-waves with Rayleigh-waves is also a problem. Picking fundamental modes dispersion curves of Rayleigh-waves is easy and fast. Therefore, the MASW method is a potential alternative to the GLI method for modeling S-wave velocities of the near surface.
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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 | 0.000 | 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) | 0.001 | 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".