Nonlinear Multiple Earthquake Location and Velocity Estimation in the Canadian Rocky Mountain Trench
Bibliographic record
Abstract
ABSTRACT The calculation of earthquake hypocenters requires careful treatment, particularly when prior knowledge of the study area is limited. The prior knowledge, such as wave velocity and data noise, is often assumed to be known in earthquake location algorithms. Such assumptions can greatly simplify the inverse problem but are less general than nonlinear approaches. A nonlinear treatment is of particular importance when the uncertainty quantification of locations is of interest. We present a nonlinear multiple-earthquake location method that is applicable when little prior knowledge of the area exists. Efficient Markov chain Monte Carlo (MCMC) sampling is employed in conjunction with a hierarchical Bayesian model that treats earthquake hypocenter parameters, as well as P-wave velocity, ratio in P-/S-wave velocities, and P- and S-data noise standard deviations as unknown. Hypocenters for multiple earthquakes are located concurrently to provide sufficient constraints for the parameter’s P-wave velocity, ratio in P-/S-wave velocity, and P- and S-data noise standard deviations, which are shared among events. The algorithm is applied to simulated and field data. With field data, 47 event hypocenters are located in 1 yr of data from 10 sensors in the Canadian Rocky Mountain trench. To analyze the probabilistic solutions, we compare single-earthquake and multiple-earthquake locations for the 47 events and find that the multiple-earthquake location produces better-constrained solutions when compared with the single-event case. In particular, depth uncertainties are significantly reduced for the multiple-earthquake location. The algorithm is inexpensive, considering that it is based on an MCMC approach and highly objective, requiring little practitioner choice for tuning.
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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".