Simulation of Shallow Natural Gas Distribution in Tunnel Construction Area and Calculation of Elasticity Parameters Based on Pre-stacked AVO Inversion Technology
Bibliographic record
Abstract
Tunnel gas and shallow natural gas overflow have been a major problem plaguing the safe construction of tunnels and one of the main types of common diseases in tunnel engineering.The article chooses the tunnel construction of Funci Highway as the research object, and collects the rock and gas data in the research area on the basis of analyzing the distribution characteristics of shallow natural gas.Based on the AVO analysis technique, the PP wave reflection coefficient is approximated as a linear combination of longitudinal wave velocity, transverse wave velocity, density and other elastic constants to construct a pre-stack AVO inversion model to analyze the shallow natural gas distribution in the Funci Highway Tunnel construction.The porosity of the rock layer in the tunnel construction area ranges from 4.5% to 12%, with an average porosity of 8.93% and a maximum permeability of 0.004 μm².The longitudinal wave impedance distribution of the non-reservoir surrounding rock ranges from 1.48 to 2.01, and the error between the longitudinal wave velocity and density obtained by the inversion and the original logging curves is up to only 2.04%.Combined with the logging data, it can realize the comprehensive evaluation of the oil and gas geological environment of Funci Highway tunnel construction, and provide data support for ensuring the safety of Funci Highway tunnel construction.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".