Characterization of marine shale in Western Hubei Province based on unmanned aerial vehicle oblique photographic data
Bibliographic record
Abstract
The marine shale in the Sinian Doushantuo Formation of Qinglinkou outcrop section is well developed, but the current characterization methods for outcrops are unsatisfactory. In this paper, the data of outcrop in the field study area were collected by Unmanned Aerial Vehicle, then processed and interpreted by oblique photography technology combined with manual investigation. Subsequently, we established a quantitative geological knowledge database of the shale formations and carried out the typical section of anatomy analysis. The results showed that the high-precision image information captured by unmanned aerial vehicle oblique photography technology can be well coupled with a three-dimensional coordinate system. The three-dimensional digital model was used to characterize the lithologic assemblage, thickness and distribution characteristics of the target reservoir. Based on this digital model, we established the three-dimensional lithology and the total organic carbon models of the outcrop area. The spatial distribution characteristics of interbedding between marine dolomite and shale in the outcrop area were displayed, and the distribution of total organic carbon was revealed under lithological constraints. The models are beneficial for the analysis and prediction of the lithology and total organic carbon, which is of great significance to the understanding of shale gas sweet spots. Cited as: Yin, S., Feng, K., Nie, X., Chen, Q., Liu, Y., Wang, P. Characterization of marine shale in Western Hubei Province based on unmanned aerial vehicle oblique photographic data. Advances in Geo-Energy Research, 2022, 6(3): 252-263. https://doi.org/10.46690/ager.2022.03.08
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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 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".