Overcoming thrust‐belt imaging problems in Magdalena Valley, Colombia
Bibliographic record
Abstract
Imaging complex geologic structures requires accurate velocity analysis and migration that will image steeply dipping strata and faults. Combine these requirements with hundreds of metres of topographic relief, strong lateral‐velocity variation at surface, and steeply dipping anisotropic strata in the overburden and you have the Canadian Foothills imaging problem. Compound these issues with lava flows at the surface, limited penetration and illumination of seismic energy, and a more dramatic tectonic history and you are imaging geologic structures in the Colombian Foothills. This 2D processing case history outlines our struggles with the noise that nearly overwhelmed the limited subsurface illumination and velocity model building under a low signal‐to‐noise condition in a complex geologic setting. Throughout this process, close interaction between interpreter and processor was critical for velocity‐model interpretation and for discriminating between signal and noise throughout the project. Where we could define the overburden dip accurately, anisotropic Kirchhoff depth migration yielded improved imaging over the time migration. In other areas with limited signal and high noise, we found the noise generated too much Kirchhoff‐operator noise. We tested Gaussian Beam migration on these datasets with promising results for this migration algorithm in noisy rough‐topography settings.
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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) | 0.017 | 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".