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 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 | 0.000 | 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 | 0.000 |
| 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 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".