Bibliographic record
Abstract
Automatic horizon tracking is an essential productivity tool for 3D seismic interpretation. Several methods are commercially available to perform this task. However, these methods fail in areas of rapidly changing structure or reflection character. Human intervention and editing in such situations slows the productivity. We propose an auto-tracker that adapts to variations of structure, changes in wavelet shape and statistics of signal amplitude. It tracks horizons as patches of constant dip where the patch extent varies according to the rate of change of dip. It allows a degree of change in wavelet shape within a patch, and adapts to change of amplitude according to local statistics. The dip steering function is the spatial gradient of continuous phase spectrum that is derived from a spectral decomposition technique known as Continuous Amplitude and Phase Spectrum ‘CAPS’. The techniquecomputes Fourier Transform of a short-time signal with high frequency resolution. The proposed approach is applied to real 3D seismic volumes from spatially varying structures and changing signal shapes and amplitudes. The auto-tracker successfully steered across the changing structures and varying amplitudes to pick horizons.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Signal processing method for automatic horizon tracking in 3D seismic interpretation; the object is a geophysical interpretation tool.
This work develops an automated seismic horizon tracker for geophysical interpretation, not metaresearch.
Seismic interpretation algorithm for automatic horizon tracking; domain geophysics tool.
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) | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".