Hybrid machine learning framework for horizontal site amplification factors estimation using H/V of microtremors and earthquakes

Reliable estimation for the horizontal seismic site amplification factors (HSAF) is an essential step in the deterministic seismic hazard analyses. The current study introduces newly hybrid machine learning (ML) framework to estimate the HSAF using solely the horizontal-to-vertical spectral ratios of earthquakes (EHVSR) and microtremors (MHVSR). This hybrid ML framework consists of random forest (RF), gradient boosting (GB), and artificial neural network of PyTorch library (ANN PT ). The final predicted HSAF is effectively reproduced from one training algorithm to another, following the newly introduced implementation for the concept of multi-stage ensemble framework. We used two types of training datasets. Regional dataset consists of EHVSRs at 75 earthquake stations in Egypt. Local dataset consists of MHVSRs at 103 microtremor measurement sites in the Nile Delta basin, northern Egypt. Thus, we can achieve two hybrid ML models, namely regional and local models. Additionally, we combined both regional and local datasets to achieve a combined model. We evaluated the predictive performance of these three models using target or unseen datasets, which comprise MHVSRs at 70 microtremor measurement sites, to examine the potential of applying these models to any given site. Another improvement to the hybrid RF→GB→ANN PT framework is proposed by introducing a constrained condition for the predicted HSAF to be ≤ three times the MHVSR. Thus, we can achieve the best predictive performance using the combined training dataset. The quantitative indicators of confidence, root mean square error, and mean absolute error are 96.5%, 1.01, and 0.78, respectively.