A novel geophysics and fractal-based approach for predicting engineering geological structures in subsurface underground engineering
Bibliographic record
Abstract
Abstract Constructing underground structures in coastal regions poses significant challenges, particularly due to seawater intrusion, which can cause corrosion and threaten the safety and stability of the caverns and surrounding facilities. A crucial aspect of preventing seawater intrusion lies in accurate mapping of the geological structure of the reservoir area and its proximity to the coastline. This study uses reflection seismic data, borehole ultrasonic imaging, and core samples to identify geological features that influence subsurface stability. The seismic profile revealed a V-shaped or concave-down structure associated with faults, suggesting a down-dropped block within the subsurface. Seismic facies analysis identified chaotic, high-amplitude reflections within basement rocks, indicating highly fractured and faulted zones, possibly including mylonitic rocks. A novel approach is proposed that combines borehole ultrasonic imaging with fractal theory, integrating core photos, seismic attributes, and geophysical analysis. A functional relationship was established between the joint surface density and the joint information dimension within the borehole. Additionally, a relationship was established between fault information dimension and borehole joint surface density. Results showed that the joint information dimensions within the identified fault zones consisttently exceeded 1.775. By applying a threshold of joint information dimension greater than 1.775, 15 small-scale structural prediction zones were identified. Subsequent analysis of core photos from the predicted regions confirmed the presence of relatively long fractured zones, demonstrating the high accuracy of the proposed method in identifying small-scale structures. This study presents a comprehensive method for mapping geological structures in coastal areas, providing an essential reference for the identification and management of small-scale features in underground engineering projects.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| 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 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".