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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/2410.06120 |
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| _version_ | 1866916428353896448 |
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| author | Messuti, Giovanni Amoroso, ortensia Napolitano, Ferdinando Falanga, Mariarosaria Capuano, Paolo Scarpetta, Silvia |
| author_facet | Messuti, Giovanni Amoroso, ortensia Napolitano, Ferdinando Falanga, Mariarosaria Capuano, Paolo Scarpetta, Silvia |
| contents | Deep learning models have demonstrated remarkable success in various fields, including seismology. However, one major challenge in deep learning is the presence of mislabeled examples. Additionally, accurately estimating model uncertainty is another challenge in machine learning. In this study, we develop Convolutional Neural Networks (CNNs) to classify seismic waveforms based on first-motion polarity. We trained multiple CNN models with different settings. We also constructed ensembles of networks to estimate uncertainty. The results showed that each training setting achieved satisfactory performances, with the ensemble method outperforming individual networks in uncertainty estimation. We observe that the uncertainty estimation ability of the ensembles of networks can be enhanced using dropout layers. In addition, comparisons among different training settings revealed that the use of dropout improved the robustness of networks to mislabeled examples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_06120 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Uncertainty estimation via ensembles of deep learning models and dropout layers for seismic traces Messuti, Giovanni Amoroso, ortensia Napolitano, Ferdinando Falanga, Mariarosaria Capuano, Paolo Scarpetta, Silvia Machine Learning Data Analysis, Statistics and Probability Deep learning models have demonstrated remarkable success in various fields, including seismology. However, one major challenge in deep learning is the presence of mislabeled examples. Additionally, accurately estimating model uncertainty is another challenge in machine learning. In this study, we develop Convolutional Neural Networks (CNNs) to classify seismic waveforms based on first-motion polarity. We trained multiple CNN models with different settings. We also constructed ensembles of networks to estimate uncertainty. The results showed that each training setting achieved satisfactory performances, with the ensemble method outperforming individual networks in uncertainty estimation. We observe that the uncertainty estimation ability of the ensembles of networks can be enhanced using dropout layers. In addition, comparisons among different training settings revealed that the use of dropout improved the robustness of networks to mislabeled examples. |
| title | Uncertainty estimation via ensembles of deep learning models and dropout layers for seismic traces |
| topic | Machine Learning Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2410.06120 |