Overcoming complex near-surface conditions for improved seismic imaging in Tarim Basin, China
Bibliographic record
Abstract
Abstract Seismic imaging in the Tarim Basin, China, presents significant challenges due to complex near-surface conditions, including desert environments, foothills, and loess mountains. These challenges are comparable to those found in regions such as the Arabian Peninsula, North Africa, the Andes Mountains, and other complex terrains worldwide. Recent advancements in seismic imaging aimed at overcoming these obstacles include: (1) advanced constrained near-surface tomography, which has significantly enhanced the robustness of near-surface velocity-depth model estimation, leading to improved resolution of deep reservoir images; (2) a recent ultra-long-offset (greater than 15 km) experiment in the foreland basin of the Tarim Oilfield, demonstrating that more accurate and deeper near-surface velocity models can be generated on land using turning-ray tomography; (3) finite-frequency wavepath tomography, which has been proven through field data examples to be a robust alternative to traditional full-waveform inversion for land seismic data; and (4) integrated tomography, which combines diving waves and reflected seismic data to develop a comprehensive velocity model from shallow to deep sections for anisotropic prestack depth migration from topography. These technologies, developed for and applied to the Tarim Basin, can be applied, with modifications, to other land basins around the world.
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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 | 0.000 |
| 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 | 0.001 | 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".