Imaging through shallow gas: Integrating broadband acquisition, processing and high-end model building for improved imaging of deeper targets
Bibliographic record
Abstract
We present a case study offshore Malaysia, shallow gas features in the overburden distort the seismic imaging at the target level. While a multifaceted approach involving a combination of seismic acquisition and processing strategies were used to improve the bandwidth of the seismic data, particularly for the low-frequency content of the seismic image, several distortions still existed at the target level. The prominent structural sag evident at the reservoir level is a typical indication that the overlying shallow gas velocity model needed to be resolved and incorporated into a depth migration algorithm.To resolve the transversally and laterally variant velocity features in the shallow gas areas, a solution that consisted of full waveform inversion (FWI) and high-resolution reflection traveltime tomography was utilized to produce an accurate compressional velocity model. To further resolve the amplitude and phase distortions at the reservoir level due to shallow gas effects, Q tomography was incorporated into the model building phase to derive a space-variant 1/Qmodel and Q compensation was integrated within depth migration.The integrated approach of broadband receiver acquisition, data processing strategies and high-end Earth model building has cumulatively improved the imaging of the reservoir below the shallow gas anomalies.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".