An Elaborate Seismic Study of Beirut: Integrating 3D Multidisciplinary Geotechnical Model Definition, Machine Learning Enhancements, and Numerical Simulations
Bibliographic record
Abstract
In seismic hazard assessment, reliance on Vs30 proxies and 1D shear wave velocity profiles often leads to underestimated ground motion. This is particularly evident in areas with complex geological structures, such as Greater Beirut (GB). The metropolis, situated near active seismic faults, experienced significant nearby earthquakes in 551, 1202, and 1837. It is characterized by diverse soil compositions, that vary from sandy terrains to limestone formations, demanding a detailed geotechnical model for seismic hazard studies. Our research developed a comprehensive 3D geotechnical model for GB, integrating data from around 500 boreholes, 700 geophysical measurements, refined DEM, and geological insights. The model delineates variations in bedrock elevation and geological strata, some sites exhibiting sediment depths up to 80 meters. We performed an iterative data analysis by combining the horizontal to vertical spectral ratio method (H/V measurements) with borehole data. This approach enabled us to estimate the average shear wave velocity (Vs-mean) in the sedimentary layer and the depth of the bedrock across the model. To address data gaps in southern GB, we used a Random Forest machine learning model, trained on interpolated points from Kriging in the central model part, ensuring continuous representation of sedimentary units even in data-limited areas. Ongoing work involves seismic simulations predicting ground motion amplification in Beirut. Using a 3D hexahedral mesh generated via Python code, we will conduct full 3D numerical simulations of seismic wave propagation. These simulations aim to provide insights into Beirut's seismic response, contributing to earthquake preparedness and risk mitigation. We will present preliminary results in predicting the seismic motion in Greater Beirut using SPECFEM3D, a spectral-element method software designed for 3D seismic wave propagation simulations.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".