Neural Network Based Attenuation of Strong Motion Peaks in Europe
Bibliographic record
Abstract
Artificial Neural Network (ANN) is used in this article to develop attenuation relationships for three peak ground motion parameters, namely, peak ground acceleration (PGA), peak ground velocity (PGV), and peak ground displacement (PGD). This article demonstrates the capability of ANN to capture the key physical aspects of seismic wave attenuation and region specific earthquake characteristics. Limited strong ground motion data and no particular functional form except for few constraints are used in the development of ANN based attenuation relationships. The database consists of 358 records (2 horizontal components of ground acceleration at each station) from 42 European shallow earthquakes. The surface magnitude (Ms), distance of site from surface projection of the rupture (R), and broad categories of soil type (soft soil, stiff soil, and rock formation) are the three input parameters. The Ms ranges from 5.5–7.9 and R ranges from 3 – 260 Km. The model is trained using 75% (134 data points) of the total data, while the remaining 25% (45 data points) of the total data is used to test the performance of the trained neural network models. The ANN is able to derive attenuation relationships which are consistent with the theory of ground motion attenuation phenomena. ANN can, therefore, be used as an alternative method to conventional regression techniques for developing attenuation relations, particularly for regions where limited earthquake data is available.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".