Increases in Life‐Safety Risks to Building Occupants from Induced Earthquakes in the Central United States
Bibliographic record
Abstract
Earthquake occurrence rates in some parts of the Central United States have been elevated for a number of years; this increase has been widely attributed to deep wastewater injection associated with oil and gas activities. This induced seismicity has caused damage to buildings and infrastructure and substantial public concern. In March 2016, the U.S. Geological Survey (USGS) published its first earthquake ground motion hazard model that accounts for the elevated seismicity, producing a one‐year forecast encompassing both induced and natural earthquakes. To assess the potential impact of the elevated seismicity on buildings and the public, this paper quantifies forecasted risks of (1) building collapse and (2) the falling of nonstructural building components by combining the 2016 USGS hazard model with fragility curves for generic modern code‐compliant buildings. The assessment shows significant increases in both types of risk compared to that caused by noninduced earthquakes alone; the magnitude of the increases varies from a few times to more than 100 times, depending on location, building period (which is correlated to building height), alternatives for the hazard model, and type of risk of interest. For exploratory purposes only, we also estimate revised values of the risk‐targeted ground motion that are currently used for designing buildings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".