A Novel Video Logging Method based on the Self-Focus Lens Array
Bibliographic record
Abstract
At present, down-hole video logging method is used to observe the bottom and lateral well wall image, which places video camera on the bottom of logging instrument. This method can acquire the bottom image clearly. It’s difficult to obtain lateral image because optic axis of lenses is placed along with the well axis, and it’s impossible to place an existing camera along the radial direction because of restriction of borehole diameter or pipe diameter and object distance of camera, etc. A method and instrument for acquiring lateral image is presented on this paper, multiple self-focus lenses are placed along radial direction, used special relay lens transmitting the multiple imaging to an image sensor, and formed one image, then transmitted the image to ground. In order to use optic spectral properties, light cone circling the self-focus lenses is used to transfer the image, the minimum overlap radius for measurement boreholethe is analyzed,the seal problem for the optical system is designed.The lateral well wall image is acquired through researching the method of lateral multiple lens, then the phase correction method is used to fuse the image from different angles of lateral well wall. The video well logging instrument is developed using the above method, which can real-timely display the crack opening , filling substance, porosity and rock component of down-hole casing and borehole.So this method can provide an interpretation tools for the pipe internal or downhole phenomena. Key words : Lateral video logging; Image acquisition; Self-focus lens array; Circular light source; Phase correction method; Image fusion
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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) | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".