# Deep Sequence Models for Predicting Average Shear Wave Velocity from Strong Motion Records

arXiv: https://arxiv.org/abs/2503.05224 · HTML: https://mlsanity.com/paper/2503.05224/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 38 references that are in the map.
- First posted on arXiv 7 Mar 2025.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [One Fits All:Power General Time Series Analysis by Pretrained LM](https://mlsanity.com/paper/2302.11939/) (2023)
- [Multi-Patch Prediction: Adapting LLMs for Time Series Representation Learning](https://mlsanity.com/paper/2402.04852/) (2024)
- [Bayesian-Deep-Learning Estimation of Earthquake Location from Single-Station Observations](https://mlsanity.com/paper/1912.01144/) (2019)
- [Deep Learning-based Epicenter Localization using Single-Station Strong Motion Records](https://mlsanity.com/paper/2405.18451/) (2024)
- [Exploring Challenges in Deep Learning of Single-Station Ground Motion Records](https://mlsanity.com/paper/2403.07569/) (2024)
- [Deep Learning-based Average Shear Wave Velocity Prediction using Accelerometer Records](https://mlsanity.com/paper/2408.14962/) (2024)

## Abstract (arXiv)

This study explores the use of deep learning for predicting the time averaged shear wave velocity in the top 30 m of the subsurface (Vs30) at strong motion recording stations in T"urkiye. Vs30 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 Vs30. We believe the study provides valuable insights into improving Vs30 predictions using a CNN-LSTM framework, demonstrating its potential for improving site characterization for seismic studies. Our codes are available via this repo: https://github.com/brsylmz23/CNNLSTMDeepEQ