5D Interpolation, PSTM and AVO Inversion for Land Seismic Data
Bibliographic record
Abstract
To address the issue of inadequate sampling, typical of land seismic data, an AVO processing flow should include interpolation and prestack migration prior to the AVO inversion. It is well established that seismic data should be prestack time migrated prior to AVO yet the irregular sampling inherent in land data can introduce migration artifacts which distort the estimates of the AVO inversion. By performing 5D minimum weighted norm interpolation prior to the PSTM, the wavefield is better sampled leading to better migration and AVO results. By working in five dimensions, the algorithm can interpolate through gaps that are problematic for lower dimensional interpolators. The 5D interpolation is amplitude preserving and appears to improve the signal-to-noise ratio with minimal evidence of smearing. In order to support these assertions, a series of parallel processing test flows were performed and compared on a 3D seismic survey from Alberta, Canada with extensive well control. For each of these flows, Ostrander gathers at key wells, AVO attributes, and their ties to 29 wells were examined. The interpolation PSTM flow prior to AVO inversion produced the best correlation to the well control.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".