Improving the resolution of impedance inversion in karst systems by incorporating diffraction information: A case study of Tarim Basin, China
Bibliographic record
Abstract
Abstract Different scales of voids and cavities in karst systems demonstrate considerable potential as exploration targets in the Tarim Basin, northwest China. Numerous diffraction events exist in the seismic data above the karst reservoir in this area because of the strong impedance contrast and irregular shape of voids. The conventional impedance inversion method using migrated data as an input cannot easily estimate the location of voids and the impedance inside the voids. In this case, an alternative approach to impedance inversion that considers the diffractive component of the total wavefield and uses the unmigrated data as an input should be used. The inversion consists of a least-squares minimization of the misfit function between the observed and modeled data. Forward modeling incorporates a combination of reflection and diffraction wavefield components. The adopted method is applied to physical modeling and field data recorded above the karst reservoir. This study’s physical modeling test simulates observed field data and is performed using a high-resolution and high-fidelity 3D modeling system. Inversion results obtained by the proposed and conventional methods are compared. Physical modeling and a field data application show that the adopted impedance inversion method improves the karst location estimation and the acoustic impedance within the voids.
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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 | 0.001 | 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 | 0.001 | 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".