Advances in the application of deep learning methods to digital rock technology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Digital rock technology is becoming essential in reservoir engineering and petrophysics. Three-dimensional digital rock reconstruction, image resolution enhancement, image segmentation, and rock parameters prediction are all crucial steps in enabling the overall analysis of digital rocks to overcome the shortcomings and limitations of traditional methods. Artificial intelligence technology, which has started to play a significant role in many different fields, may provide a new direction for the development of digital rock technology. This work presents a systematic review of the deep learning methods that are being applied to tasks within digital rock analysis, including the reconstruction of digital rocks, high-resolution image acquisition, grayscale image segmentation, and parameter prediction. The results of these applications prove that state-of-the-art deep learning methods can help advance and provide a new approach to scientific knowledge in the field of digital rocks. This work also discusses future research and developments on the application of deep learning methods to digital rock technology. Cited as: Li, X., Li, B., Liu, F., Li, T., Nie, X. Advances in the application of deep learning methods to digital rock technology. Advances in Geo-Energy Research, 2023, 8(1): 5-18. https://doi.org/10.46690/ager.2023.04.02
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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 | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it