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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.07569 |
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| _version_ | 1866909600338411520 |
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| author | Çağlar, Ümit Mert Yilmaz, Baris Türkmen, Melek Akagündüz, Erdem Tileylioglu, Salih |
| author_facet | Çağlar, Ümit Mert Yilmaz, Baris Türkmen, Melek Akagündüz, Erdem Tileylioglu, Salih |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_07569 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring Challenges in Deep Learning of Single-Station Ground Motion Records Çağlar, Ümit Mert Yilmaz, Baris Türkmen, Melek Akagündüz, Erdem Tileylioglu, Salih Signal Processing Computer Vision and Pattern Recognition Machine Learning 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. |
| title | Exploring Challenges in Deep Learning of Single-Station Ground Motion Records |
| topic | Signal Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2403.07569 |