Evaluation of the southern California seismic velocity models through simulation of recorded events
Bibliographic record
Abstract
Significant effort has been devoted over the last two decades to the development of various seismic velocity models for the region of southern California, United States. These models are mostly used in forward wave propagation simulation studies, but also as base models for tomographic and source inversions. Two of these models, the community velocity models CVM-S and CVM-H, are among the most commonly used for this region. This includes two alternative variations to the original models, the recently released CVM-S4.26 which incorporates results from a sequence of tomographic inversions into CVM-S, and the user-controlled option of CVM-H to replace the near-surface profiles with a VS30-based geotechnical model. Although either one of these models is regarded as acceptable by the modeling community, it is known that they have differences in their representation of the crustal structure and sedimentary deposits in the region, and thus can lead to different results in forward and inverse problems. In this paper, we evaluate the accuracy of these models when used to predict the ground motion in the greater Los Angeles region by means of an assessment of a collection of simulations of recent events. In total, we consider 30 moderate-magnitude earthquakes (3.5 < Mw < 5.5) between 1998 and 2014, and compare synthetics with data recorded by seismic networks during these events. The simulations are done using a finite-element parallel code, with numerical models that satisfy a maximum frequency of 1 Hz and a minimum shear wave velocity of 200 m s−1. The comparisons between data and synthetics are ranked quantitatively by means of a goodness-of-fit (GOF) criteria. We analyse the regional distribution of the GOF results for all events and all models, and draw conclusions from the results and how these correlate to the models. We find that, in light of our comparisons, the model CVM-S4.26 consistently yields better results.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".