Automated Seismic Horizon Tracking Using Advance Spectral Decomposition Method
Bibliographic record
Abstract
Introduction/Importance of Study: In three-dimensional seismic interpretation, automatic horizon tracking is a critical productivity tool. However, it often fails in areas where horizons are not smooth and exhibit sharp discontinuities such as large spatial displacement or changes in reflector aliasing, horizon gradients, and signal character. Such failures require manual intervention, which increases the interpretation cycle time.Novelty Statement: In this research study, an automated horizon tracker is proposed that adapts tochangesin reflector shape, strength,and geological variation as it traverses through the seismic data volume.Material and Method: A predefined spatial grid window steers across the horizon surface where its orientation changes with the variation in a pre-computed, high-resolution, dip volume. The method is further improved to incorporate tracking horizons across discontinuities i.e. faults.Result and Discussion: The proposed method is tested on three-dimensional seismic data with varying geological conditions and has demonstrated successful mapping of horizon surfaces and effective matching across major faults.Concluding Remarks: Our automatic procedure, by reducing the need for manual intervention during interpretation, has the potential to significantly improve productivity.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".