Repurposing deep closed mines as seismic forecasting research platforms
Bibliographic record
Abstract
Seismic forecasting remains constrained by surface noise and low spatial resolution, limiting reproducible predictions. Although deep borehole stations and underground laboratories improve conditions, they face high costs, sparse coverage, and narrow disciplinary scope. In this work, the strategic reuse of deep closed mines as seismic forecasting laboratories was evaluated. Closed mines, abundant and deep with extensive tunnels and reusable infrastructure, provide ideal low-noise, near-source environments for scalable observation networks. They can lower construction costs, enable simultaneous monitoring of natural and induced earthquakes, and support comparative studies of source mechanisms and forecasting methods. Key challenges include processing massive data volumes, integrating multi-source information, and ensuring equipment reliability in harsh environments. Future directions emphasize building three-dimensional, multiphysics monitoring networks, advancing interdisciplinary and international collaboration, and developing an integrated “observation–warning–prevention” platform. Repurposing closed mines not only expands underground space utilization but also offers a potential paradigm shift in seismic monitoring, providing a novel pathway to overcome longstanding forecasting bottlenecks. Document Type: Perspective Cited as: Li, P., Tang, C., Li, Y., Zhang, Y., Gorjian, M., Cai, M. Repurposing deep closed mines as seismic forecasting research platforms. Advances in Geo-Energy Research, 2025, 18(1): 1-6. https://doi.org/10.46690/ager.2025.10.01
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".