Automated Classification of Volcanic Earthquakes Using Transformer Encoders: Insights into Data Quality and Model Interpretability

• URL: http://arxiv.org/abs/2507.01260v2
• Date: Mon, 21 Jul 2025 12:59:46 GMT
• Title: Automated Classification of Volcanic Earthquakes Using Transformer Encoders: Insights into Data Quality and Model Interpretability
• Authors: Y. Suzuki, Y. Yukutake, T. Ohminato, M. Yamasaki, Ahyi Kim,
• Abstract summary: We develop a deep learning model using a transformer encoder for a more objective and efficient classification.<n>Tested on Mount Asama's diverse seismic activity, our model achieved high F1 scores (0.930 for volcano tectonic, 0.931 for low-frequency earthquakes, and 0.980 for noise), superior to a conventional CNN-based method.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Precisely classifying earthquake types is crucial for elucidating the relationship between volcanic earthquakes and volcanic activity. However, traditional methods rely on subjective human judgment, which requires considerable time and effort. To address this issue, we developed a deep learning model using a transformer encoder for a more objective and efficient classification. Tested on Mount Asama's diverse seismic activity, our model achieved high F1 scores (0.930 for volcano tectonic, 0.931 for low-frequency earthquakes, and 0.980 for noise), superior to a conventional CNN-based method. To enhance interpretability, attention weight visualizations were analyzed, revealing that the model focuses on key waveform features similarly to human experts. However, inconsistencies in training data, such as ambiguously labeled B-type events with S-waves, were found to influence classification accuracy and attention weight distributions. Experiments addressing data selection and augmentation demonstrated the importance of balancing data quality and diversity. In addition, stations within 3 km of the crater played an important role in improving model performance and interpretability. These findings highlight the potential of Transformer-based models for automated volcanic earthquake classification, particularly in improving efficiency and interpretability. By addressing challenges such as data imbalance and subjective labeling, our approach provides a robust framework for understanding seismic activity at Mount Asama. Moreover, this framework offers opportunities for transfer learning to other volcanic regions, paving the way for enhanced volcanic hazard assessments and disaster mitigation strategies.

Related papers

• Efficient Self-Supervised Learning for Earth Observation via Dynamic Dataset Curation [67.23953699167274]
  Self-supervised learning (SSL) has enabled the development of vision foundation models for Earth Observation (EO)<n>In EO, this challenge is amplified by the redundancy and heavy-tailed distributions common in satellite imagery.<n>We propose a dynamic dataset pruning strategy designed to improve SSL pre-training by maximizing dataset diversity and balance.
  arXiv  Detail & Related papers  (2025-04-09T15:13:26Z)
• Evaluating and Explaining Earthquake-Induced Liquefaction Potential through Multi-Modal Transformers [0.0]
  This study presents an explainable parallel transformer architecture for soil liquefaction prediction.<n>The architecture processes data from 165 case histories across 11 major earthquakes.<n>The model achieves 93.75% prediction accuracy on cross-regional validation sets.
  arXiv  Detail & Related papers  (2025-02-11T09:18:07Z)
• Localized Gaussians as Self-Attention Weights for Point Clouds Correspondence [92.07601770031236]
  We investigate semantically meaningful patterns in the attention heads of an encoder-only Transformer architecture. We find that fixing the attention weights not only accelerates the training process but also enhances the stability of the optimization.
  arXiv  Detail & Related papers  (2024-09-20T07:41:47Z)
• Estimating Earthquake Magnitude in Sentinel-1 Imagery via Ranking [5.71478837100808]
  We propose to pose the estimation of earthquake magnitudes as a metric-learning problem. We train models to estimate earthquake magnitude from Sentinel-1 satellite imagery and to additionally rank pairwise samples. Our experiments show at max a 30%+ improvement in MAE over prior regression-only based methods.
  arXiv  Detail & Related papers  (2024-07-25T15:35:44Z)
• Data-Driven Prediction of Seismic Intensity Distributions Featuring Hybrid Classification-Regression Models [21.327960186900885]
  This study develops linear regression models capable of predicting seismic intensity distributions based on earthquake parameters. The dataset comprises seismic intensity data from earthquakes that occurred in the vicinity of Japan between 1997 and 2020. The proposed model can predict even abnormal seismic intensity distributions, a task at conventional GMPEs often struggle.
  arXiv  Detail & Related papers  (2024-02-03T13:39:22Z)
• FaultSeg Swin-UNETR: Transformer-Based Self-Supervised Pretraining Model for Fault Recognition [13.339333273943842]
  This paper introduces an approach to enhance seismic fault recognition through self-supervised pretraining. We have employed the Swin Transformer model as the core network and employed the SimMIM pretraining task to capture unique features related to discontinuities in seismic data. Experimental results demonstrate that our proposed method attains state-of-the-art performance on the Thebe dataset, as measured by the OIS and ODS metrics.
  arXiv  Detail & Related papers  (2023-10-27T08:38:59Z)
• Classification of structural building damage grades from multi-temporal photogrammetric point clouds using a machine learning model trained on virtual laser scanning data [58.720142291102135]
  We present a novel approach to automatically assess multi-class building damage from real-world point clouds. We use a machine learning model trained on virtual laser scanning (VLS) data. The model yields high multi-target classification accuracies (overall accuracy: 92.0% - 95.1%)
  arXiv  Detail & Related papers  (2023-02-24T12:04:46Z)
• Improving robustness of jet tagging algorithms with adversarial training [56.79800815519762]
  We investigate the vulnerability of flavor tagging algorithms via application of adversarial attacks. We present an adversarial training strategy that mitigates the impact of such simulated attacks.
  arXiv  Detail & Related papers  (2022-03-25T19:57:19Z)
• DeepAdversaries: Examining the Robustness of Deep Learning Models for Galaxy Morphology Classification [47.38422424155742]
  In morphological classification of galaxies, we study the effects of perturbations in imaging data. We show that training with domain adaptation improves model robustness and mitigates the effects of these perturbations.
  arXiv  Detail & Related papers  (2021-12-28T21:29:02Z)
• Towards advancing the earthquake forecasting by machine learning of satellite data [22.87513332935679]
  We develop a novel machine learning method, namely Inverse Boosting Pruning Trees (IBPT), to issue short-term forecast based on the satellite data of 1,371 earthquakes of magnitude six or above. Our proposed method outperforms all the six selected baselines and shows a strong capability in improving the likelihood of earthquake forecasting across different earthquake databases.
  arXiv  Detail & Related papers  (2021-01-31T02:29:48Z)
• From Sound Representation to Model Robustness [82.21746840893658]
  We investigate the impact of different standard environmental sound representations (spectrograms) on the recognition performance and adversarial attack robustness of a victim residual convolutional neural network. Averaged over various experiments on three environmental sound datasets, we found the ResNet-18 model outperforms other deep learning architectures.
  arXiv  Detail & Related papers  (2020-07-27T17:30:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.