Browsing by Author "Yilmaz, B."

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Citation - Scopus: 3
Deep Learning-Based Average Shear Wave Velocity Prediction Using Accelerometer Records
(International Association for Earthquake Engineering, 2024) Yilmaz, B.; Türkmen, M.; Meral, S.; Akagündüz, E.; Tileylioglu, S.
Assessing seismic hazards and thereby designing earthquake-resilient structures or evaluating structural damage that has been incurred after an earthquake are important objectives in earthquake engineering. Both tasks require critical evaluation of strong ground motion records, and the knowledge of site conditions at the earthquake stations plays a major role in achieving the aforementioned objectives. Site conditions are generally represented by the time-averaged shear wave velocity in the upper 30 meters of the geological materials (Vs30). Several strong motion stations lack Vs30 measurements resulting in potentially inaccurate assessment of seismic hazards and evaluation of ground motion records. In this study, we present a deep learning-based approach for predicting Vs30 at strong motion station locations using three-channel earthquake records. For this purpose, Convolutional Neural Networks (CNNs) with dilated and causal convolutional layers are used to extract deep features from accelerometer records collected from over 700 stations located in Turkey. In order to overcome the limited availability of labeled data, we propose a two-phase training approach. In the first phase, a CNN is trained to estimate the epicenters, for which ground truth is available for all records. After the CNN is trained, the pre-trained encoder is fine-tuned based on the Vs30 ground truth. The performance of the proposed method is compared with machine learning models that utilize hand-crafted features. The results demonstrate that the deep convolutional encoder based Vs30 prediction model outperforms the machine learning models that rely on hand-crafted features. This suggests that our computational model can extract meaningful and informative features from the accelerometer records, enabling more accurate Vs30 predictions. The findings of this study highlight the potential of deep learning-based approaches in seismology and earthquake engineering. © 2024, International Association for Earthquake Engineering. All rights reserved.
Exploring Challenges in Deep Learning of Single-Station Ground Motion Records
(Springer Science and Business Media Deutschland GmbH, 2025) Çaǧlar, Ü.M.; Yilmaz, B.; Türkmen, M.; Akagündüz, E.; Tileylioglu, S.
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 “deep” 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. © 2025 Elsevier B.V., All rights reserved.