The use of seismic modeling for the geologic interpretation of deep seismic reflection data with low signal-to-noise ratios
Bibliographic record
Abstract
We have performed a deep seismic reflection study, DACIA-PLAN, based on the data recorded along a crooked line across the southeastern Romanian Carpathians. The signal-to-noise ratio (S/N) of these data varies along the seismic profile, and its variation is considered to be an effect of the rough topography, complex subsurface geology, and varying surface conditions encountered during seismic data acquisition. The migrated time section that covers the mountainous area is clear, without visible reflections, making the geologic interpretation very difficult. We used a seismic modeling technique to explain the poor S/N of the recorded data and to generate synthetic seismic sections that can be useful for the geologic interpretation of the field seismic section (migrated time section). We used ray-tracing modeling to obtain the expected seismic expression of horizons of interest. Subsurface illumination modeling indicates that the complex subsurface geology and irregularly deployed sources and receivers are responsible for the incomplete and/or uneven illumination of the subsurface and can lead to strong amplitude variations. We then used 2.5D acoustic finite-difference modeling to analyze the effect of a crooked line on seismic wave propagation. The synthetic shot gathers prove that crooked line arrival times for reflected and head waves contain static time shifts relative to a straight line regular sampling geometry. Some geologic interfaces of interest are not well-imaged on the synthetic seismic section, and this is considered to be an effect of poor positioning during seismic data acquisition. We used the velocity model from the tomographic inversion of first-arrival traveltimes and synthetic and field crooked line deep seismic reflection data to create a structural image for the southeastern Romanian Carpathians and the Focsani Basin, which tie well with the geologic model built for this area on the basis of geologic and well data only.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".