Sand moisture critically determines buried gas pipeline leak patterns: insights from fiber-optic vibration–thermal signatures
Bibliographic record
Abstract
Subsurface gas pipeline leaks present critical safety risks governed by soil moisture-regulated multiphase dynamics. This study employs laboratory experiments integrated with dual-modality distributed fiber-optic sensing, simultaneously acquiring vibration and temperature signatures, to establish moisture-dependent failure criteria. Subsurface gas leakage from pressurized pipelines generates two distinct failure modes through soil interaction: spewing leaks characterized by crater-forming gas jets, and diffusion leaks marked by gradual pore-scale migration. Experimental results demonstrate that these regimes are governed by soil moisture content: (1) Under arid conditions (<5% moisture content), spewing leaks manifest through violent gas ejection of particulate matter, producing intense vibration amplitudes, and rapid thermal transients. (2) Conversely, in moisture-saturated sands (5%–15% moisture content), diffusion leakage predominates, characterized by gas percolation through evolving cavity–fissure networks that induce dual vibration mechanisms—soil matrix deformation and gas–granular interactions—coupled with moderated cooling rates. The observed transition between spewing and diffusion leak patterns, governed by soil moisture levels, highlights the need for dynamic detection protocols in pipeline integrity management. These findings advance mechanistic understanding of subsurface gas transport phenomena while offering potentially actionable guidelines for optimizing fiber-optic monitoring systems in heterogeneous soil environments.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".