Processing of Sparsely and Irregularly Sampled 3D OBC Seismic Data Offshore Abu Dhabi
Bibliographic record
Abstract
Abstract An OBC technique generally provides several technical advantages over a conventional towed streamer survey. However, due mainly to commercial and operational constraints, some compromises on survey parameters are required though dense and symmetric sampling is ideal. As a result, seismic data is often sparsely and irregularly sampled, leading to several challenges in processing of OBC seismic data offshore Abu Dhabi. Conventional linear noise attenuations are not effective with Scholte waves as they are usually aliased with typical source and receiver sampling intervals in 3D OBC seismic data, and sometimes scattered because of near-surface heterogeneity offshore Abu Dhabi. To address this, we apply model-based surface wave attenuation, Surface Wave Analysis Modeling and Inversion (SWAMI), which enables an estimate of local near-surface properties and create noise model by analyzing dispersion curves. The method does not involve multi-channel filtering to the input data so both direct and scattered Scholte waves are effectively attenuated without suffering a lack of spatial sampling. Matching Pursuit Fourier Interpolation (MPFI) is then implemented to enhance spatial sampling caused by acquisition geometry. MPFI is a frequency domain interpolation and regularization technique. Iterative process along with its anti-aliasing capability enables optimum data reconstruction for each frequency range at desired locations. In addition to regularization aspect, MPFI is targeted to densify receiver line interval and extend source lines with 5D implementation (4 spatial coordinates and time). This consequently enhances fold, offset and azimuth distributions of the data. SWAMI for both direct and scattered Scholte waves and 5D MPFI are successfully implemented to address processing challenges related to insufficient spatial sampling in 3D OBC seismic data offshore Abu Dhabi. SWAMI deals with complex properties of Scholte waves and attenuates both direct and scattered ones without any processing artefacts. MPFI with 5D implementation dramatically improves spatial sampling. The several aspects of values of interpolation are recognized such as improvement of signal to noise ratio and stack response. Additionally, the interpolated data provides great potential to subsequent processes such as de-noise, de-multiple and imaging. The results enhance the value of sparsely and irregularly sampled OBC seismic data, and indicate a possibility that seismic data with insufficient spatial sampling could achieve an equivalent data quality to dense and full azimuth survey.This consequently allows us to ease survey requirements related to spatial sampling criteria. Unlike a land case, these two techniques had not previously been applied to OBC data. This study, therefore, proved their applicability, reliability and benefits to 3D OBC survey.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".