The Development of a Local Ground Motion Prediction Equation from Recorded Data
Bibliographic record
Abstract
A representative attenuation relationship is one of the key components required in seismic hazard assessment of a region of interest.In this project, a ground-motion attenuation relationship for peak ground acceleration was developed for Sharjah, United Arab Emirates (UAE) region.Incorporated Research Institutions for Seismology (IRIS) as well as Building and Housing Research Center (BHRC) databases were utilized to collect strong ground motion of 90 horizontal component waveforms from different earthquakes measured by 440 stations in Iran and Sharjah.The collected dataset is composed from earthquakes that occurred in Iran with attenuation reaching UAE in moment magnitude varying from 4 to 7.3.The relationships derived are for distances up to 100 km, in a time period from 2008 to 2018.Attributes considered for each earthquake include earthquake date, time of occurrence, moment magnitude, depth, epicentral distance, acceleration time series, peak ground acceleration (PGA), and time shear velocity as well as event location and coordinates.A set of statistical analysis techniques was used to analyze and validate earthquake records.In this study, different attenuation relationship equations utilized for similar regions were collected from literature of previous work done in multiple countries.Based on the collected equations, new equations that are more suitable for Sharjah were developed by applying nonlinear regression analysis using Statistical Package for the Social Sciences (SPSS) statistics software and MATLAB.Accordingly, an optimum model was formulated that best suits Sharjah study area characteristics.The developed ground-motion prediction equations derived in the study can be used to predict earthquake-prone locations in UAE and other locations with similar characteristics.Additional artificial neural network (ANN) calculations were generated to verify the attenuated PGA mode in Sharjah based on Iranian attributes.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".