Near-Real-Time Seismic Monitoring for Pipelines
Bibliographic record
Abstract
Earthquake hazard management for oil and gas pipelines should include both preparedness and response. The typical approach for management of seismic hazards for pipelines is to determine where large ground motions are frequently expected, and apply mitigation to those pipeline segments. The approach presented in this paper supplements the typical approach but focuses on what to do, and where to do it, just after an earthquake happens. In other words, we ask and answer: “Is the earthquake we just had important?”, “What pipeline is and what sites might it be important for?”, and “What should we do?” In general, modern, high-pressure oil and gas pipelines resist the direct effects of strong shaking, but are vulnerable to large co-seismic differential permanent ground displacement (PGD) produced by surface fault rupture, landslides, soil liquefaction, or lateral spreading. The approach used in this paper employs empirical relationships between earthquake magnitude, distance, and the occurrence of PGD, derived from co-seismic PGD case-history data, to prioritize affected pipeline segments for detailed site-specific hazard assessments, pre-event resiliency upgrades, and post-event response. To help pipeline operators prepare for earthquakes, pipeline networks are mapped with respect to earthquake probability and co-seismic PGD susceptibility. Geological and terrain analyses identify pipeline segments that cross PGD-susceptible ground. Probabilistic seismic models and deterministic scenarios are considered in estimating the frequency of sufficiently large and close causative earthquakes. Pipeline segments are prioritized where strong earthquakes are frequent and ground is susceptible to co-seismic PGD. These may be short-listed for mitigation that either reduces the pipeline’s vulnerability to damage or limits failure consequences. When an earthquake occurs, pipeline segments with credible PGD potential are highlighted within minutes of an earthquake’s occurrence. These assessments occur in near-real-time as part of an online geohazard management database. The system collects magnitude and location data from online earthquake data feeds and intersects them against pipeline network and terrain hazard map data. Pipeline operators can quickly mobilize inspection and response resources to a focused area of concern.
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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 | 0.000 |
| 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) | 0.000 | 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".