Automated Amplitude and Phase Attribute-Based Horizon Picking Applied to 3-D Sub-bottom Data
Bibliographic record
Abstract
The 3-D sub-bottom profiler (SBP) is widely used for observing sub-bottom structures due to its high resolution and spatial coverage. However, traditional picking methods are limited by scattering noise and imbalanced intensity, resulting in poor picking of reflections with weak intensities and overpicking due to scattering noise. This limitation has hindered the development of 3-D SBP processing technologies relative to the widespread applications. In this article, we present a 3-D SBP horizon-picking method that takes into account both amplitude and phase information. First, we apply an amplitude data enhancement filtering algorithm considering the plate-like structure and the nonvertical characteristic to avoid scattering noise interference and highlight plate-like horizon features. Subsequently, a threshold-based algorithm is applied to pick the amplitude horizons, reflecting the main structures of layer interfaces based on the processed amplitude data. Then, the local phase information of the SBP data is derived through the monogenic analysis, and phase horizons are picked, which can describe detailed reflections with weak intensities, although overpicking may occur. To leverage both amplitude and phase horizons, a combination method is proposed to ensure continuous and fine horizon picking containing both main and detailed 3-D SBP horizon structures. The method was validated by comparing automated horizon results with manual results using experimental data. A total of 87% and 86% F-measures were achieved on two data sets, respectively.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".