This study explores the use of deep learning for predicting the time averaged shear wave velocity in the top 30 m of the subsurface ( \(V_\textrm{s30}\) ) at strong motion recording stations in Türkiye. \(V_\textrm{s30}\) is a key parameter in site characterization and, as a result for seismic hazard assessment. However, it is often unavailable due to the lack of direct measurements and is therefore estimated using empirical correlations. Such correlations however are commonly inadequate in capturing complex, site-specific variability and this motivates the need for data-driven approaches. In this study, we employ a hybrid deep learning model combining convolutional neural networks (CNNs) and long short-term memory (LSTM) networks to capture both spatial and temporal dependencies in strong motion records. Furthermore, we explore how using different parts of the signal influence our deep learning model. Our results suggest that the hybrid approach effectively learns complex, nonlinear relationships within seismic signals. We observed that an improved P-wave arrival time model increased the prediction accuracy of \(V_\textrm{s30}\) . We believe the study provides valuable insights into improving \(V_\textrm{s30}\) predictions using a CNN-LSTM framework, demonstrating its potential for improving site characterization for seismic studies. Our codes are available via this https://github.com/brsylmz23/CNNLSTM_DeepEQ GitHub repository.
