2024/10/08 by Giovanni Messuti, Messuti, Giovanni, Ortensia Amoroso +9 · 1 citation
Computer Science · Earth and Planetary Sciences · Engineering · #Data Analysis #Drilling and Well Engineering #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Seismic Imaging and Inversion Techniques #Seismology and Earthquake Studies #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.2410.06120
openalex publication_date 2024/10/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.