Innovative approach to green mining: Integration of drone technology, GIS, and ERT for basalt extraction and CO2 storage in Bagrote Valley, Gilgit-Baltistan, Pakistan
Bibliographic record
Abstract
This study investigated the dual potential of basalt deposits for dimensional stone quarrying and CO 2 sequestration. The integrated approach combined unmanned aerial vehicle (UAV) surveys for surface modeling; electrical resistivity imaging (ERI), particularly vertical electrical sounding (VES) surveys for subsurface modeling; and a geographic information system (GIS) for area, volume, tonnage, and phase design. UAVs, in conjunction with GIS, provide high-resolution 3D models including digital terrain models (DTMs), digital elevation models (DEMs), and contour maps. These models were used to design a primary quarry while optimizing dimensional stone extraction and minimizing waste. The total area of the deposit is 1.46 km 2 , the volume is 88.08 × 10 6 m 3 , and the total extractable material is 255.45 × 10 6 t across eight phases. The ERI/VES survey identified three distinct lithological layers: fresh, fractured, and weathered basalts. The fresh basalt zone guides the extraction strategies for dimensional stones, whereas the fractured zone represents the optimal target for CO 2 injection and storage. The low-resistivity weathered zone functions as an impermeable cap rock and prevents the upward migration of injected CO 2 . A geochemical analysis revealed a composition comparable to those of world-renowned CO 2 sequestration sites, with 24.6%–28.2% of the mass composed of Ca 2 + , Mg 2 + , and Fe 2 + cations. The estimated CO 2 storage capacity is 0.211 × 10 6 t, with each kilogram capable of storing 0.8 g of CO 2 . This pioneering study demonstrates the feasibility of integrating carbon capture initiatives with conventional mining operations. It presents a model for sustainable resource utilization, particularly in mountainous regions with fragile ecosystems.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".