Automated Seismic Horizon Tracking Using Advance Spectral Decomposition Method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it