Accurate Seismic Imaging Methods on Complex Fault Blocks of Subei Basin
Bibliographic record
Abstract
Subei basin is well-known for its complex fault block reservoirs and has wide growth of shallow igneous rocks. These inherent geological characteristics seriously affect the accurate seismic imaging of target layers. Pre-stack depth migration is an effective imaging method for complex structure and velocity field zones. In order to verify nice imaging ability of Kirchhoff’s pre-stack depth migration (KPSDM) and reverse-time migration (RTM), imaging tests were carried out based on the velocity model for complex fault block of igneous area in Subei basin. Experimental results showed that under the condition of reliable high-frequency velocity field, RTM had obvious advantage in imaging of fault blocks, otherwise, KPSDM was a viable option. In order to verify above experimental results, by selecting seismic data with higher signal to noise ratio (SNR), a reliable high-frequency velocity field was established, and the imaging processing was carried out. The results showed that RTM has obvious advantage over KPSDM in the aspect of improving imaging results of fault blocks and target layers under igneous rocks. Therefore, the choosing of imaging methods for complex fault blocks with igneous zone depends on SNR of seismic data. If seismic data has high SNR, a more accurate high frequency velocity field can be set up. Thus RTM can achieve precise imaging for target layers. If the seismic data has low SNR, KPSDM will become a good choice. Key words: Subei basin; Complex fault block; Igneous rock; Velocity model; Reverse time migration
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".