arXiv subjects

Baris Yilmaz

Publications and source records attributed to Baris Yilmaz.

4 recordsLinked to original sources

arXiv2025

TimesNet-Gen: Deep Learning-based Site Specific Strong Motion Generation

Effective earthquake risk reduction relies on accurate site-specific evaluations, which require models capable of representing the influence of local site conditions on ground motion characteristics. We address strong ground motion generation from time-domain accelerometer records and introduce the TimesNet-Gen, a deep generative framework. In this framework, site-specific generation is directly achieved through a station-restricted, Dirichlet-based latent space resampling strategy, without relying on explicit conditioning inputs or dimensionality reduction. Pre-trained on the AFAD dataset via self-supervised learning, the frozen model demonstrates robust cross-regional generalization by successfully generating station-specific NGA-West2 records without any fine-tuning. Model performance is evaluated by comparing the distributions of generated and real records in the log-HVSR space, alongside the joint analysis of peak ground acceleration and fundamental site frequency. As a baseline, we construct a spectrogram-based conditional variational autoencoder (CVAE) explicitly formulated for station-specific latent space modeling. The results show strong station-wise alignment, consistent cross-regional ground motion synthesis, and a favorable comparison with a spectrogram-based conditional variational autoencoder baseline, demonstrating that the model empirically maintains the essential physical coupling between frequency content and peak amplitude. Our codes are available via https://github.com/brsylmz23/TimesNet-Gen.

cs.LG↗

arXiv2025

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

This study explores the use of deep learning for predicting the time averaged shear wave velocity in the top 30 m of the subsurface ($V_{s30}$) at strong motion recording stations in T\"urkiye. $V_{s30}$ 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 $V_{s30}$. We believe the study provides valuable insights into improving $V_{s30}$ 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/CNNLSTM_DeepEQ

cs.LG↗

arXiv2024

Deep Learning-based Epicenter Localization using Single-Station Strong Motion Records

This paper explores the application of deep learning (DL) techniques to strong motion records for single-station epicenter localization. Often underutilized in seismology-related studies, strong motion records offer a potential wealth of information about seismic events. We investigate whether DL-based methods can effectively leverage this data for accurate epicenter localization. Our study introduces AFAD-1218, a collection comprising more than 36,000 strong motion records sourced from Turkey. To utilize the strong motion records represented in either the time or the frequency domain, we propose two neural network architectures: deep residual network and temporal convolutional networks. Through extensive experimentation, we demonstrate the efficacy of DL approaches in extracting meaningful insights from these records, showcasing their potential for enhancing seismic event analysis and localization accuracy. Notably, our findings highlight significant reductions in prediction error achieved through the exclusion of low signal-to-noise ratio records, both in nationwide experiments and regional transfer-learning scenarios. Overall, this research underscores the promise of DL techniques in harnessing strong motion records for improved seismic event characterization and localization.

eess.SP↗

arXiv2024

Exploring Challenges in Deep Learning of Single-Station Ground Motion Records

Contemporary deep learning models have demonstrated promising results across various applications within seismology and earthquake engineering. These models rely primarily on utilizing ground motion records for tasks such as earthquake event classification, localization, earthquake early warning systems, and structural health monitoring. However, the extent to which these models truly extract meaningful patterns from these complex time-series signals remains underexplored. In this study, our objective is to evaluate the degree to which auxiliary information, such as seismic phase arrival times or seismic station distribution within a network, dominates the process of deep learning from ground motion records, potentially hindering its effectiveness. Our experimental results reveal a strong dependence on the highly correlated Primary (P) and Secondary (S) phase arrival times. These findings expose a critical gap in the current research landscape, highlighting the lack of robust methodologies for deep learning from single-station ground motion recordings that do not rely on auxiliary inputs.

eess.SP↗