Seismology

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Seismology studies how seismic waves reveal Earth’s interior and how earthquakes and ground motion develop, supporting hazard assessment, exploration, and monitoring. Work combines physical methods such as phase picking, tomography, moment-tensor inversion, and wave-equation simulation with neural networks, transformers, self-supervised learning, and generative models for detection, denoising, prediction, and subsurface imaging. Evaluation spans earthquake catalogs and regional or teleseismic records, synthetic wavefields and velocity models, field surveys, and sparse sensor arrays, with probabilistic inference used to quantify uncertainty. A central challenge is making data-driven systems reliable across regions, instruments, geology, and noise while preserving physical consistency and signal fidelity, especially when labeled field data are scarce.

Latest papers

43 papers5 papers in the last four weeks, up 25% on the four weeks before. 0.0% of all new papers.

Evaluating PhaseNet on Teleseismic Data with MsPASS
May 10, 2026Jinxin Ma, Yinzhi Wang, Gary L. Pavlis +1Efficient Neural Network Inference Seismology
Texas Advanced Computing Center, The University of Texas at Austin · Department of Earth and Atmospheric Sciences, Indiana University, Bloomington, IN 47405
Real-Time Earthquake Magnitude Classification from Initial P-Waves: Models, Dataset, and Comparative Analysis for South Asia
May 10, 2026Md Nasiat Hasan Fahim, Md. Abid Ullah Muhib, Rayhanul Amin Tanvir +1Time Series Classification Seismology
Department of Computer Science and Engineering Shahjalal University of Science and Technology Sylhet, Bangladesh
Parameter-Efficient Adaptation of Pre-Trained Vision Foundation Models for Active and Passive Seismic Data Denoising
Apr 30, 2026Jiahua Zhao, Umair bin Waheed, Jing Sun +3Test-Time Adaptation Vision Foundation Model Adaptation
Computation-based Science and Technology Research Center, The Cyprus Institute, Nicosia, Cyprus · Department of Geosciences, College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia · Department of Intelligent Systems, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, Netherlands +2
Physical Sensitivity Kernels Can Emerge in Data-Driven Forward Models: Evidence From Surface-Wave Dispersion
Apr 5, 2026Ziye Yu, Yuqi Cai, Xin LiuNeural Surrogate Modeling Seismology
Institute of Geophysics, China Earthquake Administration, Beijing 100081, China · Laboratory of Seismology and Physics of Earth’s Interior, School of Earth and Space Sciences, University of Science and Technology of China, Hefei 230026, China · Institute of Advanced Technology, University of Science and Technology of China, Hefei 230088, China
Large Vision Model-Guided Masked Low-Rank Approximation for Ground-Roll Attenuation
Apr 1, 2026Jiacheng Liao, Feng Qian, Ziyin Fan +1Seismology Low-Rank Approximation
School of Information and Communication Engineering, Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu, 611731 China · School of Resources and Environment, Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu, 611731 China
SAGE: Subsurface AI-driven Geostatistical Extraction with proxy posterior
Mar 31, 2026Huseyin Tuna Erdinc, Ipsita Bhar, Rafael Orozco +2Seismology Conditional Generative Modeling
Georgia Institute of Technology · Oxy · Huber Engineered Materials
Transcending Classical Neural Network Boundaries: A Quantum-Classical Synergistic Paradigm for Seismic Data Processing
Mar 25, 2026Zhengyi Yuan, Xintong Dong, Xinyang Wang +2Quantum Neural Networks Hybrid Quantum-Classical ML
College of Instrumentation and Electrical Engineering, Jilin University, Changchun 130026, China.
TRACE: A Multi-Agent System for Autonomous Physical Reasoning for Seismology
Mar 22, 2026Feng Liu, Xin Cui, Jian Xu +11Physical Reasoning AI Agents for Scientific Discovery
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China. · Shanghai Artificial Intelligence Laboratory, Shanghai, China. · School of Earth and Space Sciences, University of Science and Technology of China, Hefei, China. +3
Improving moment tensor solutions under Earth structure uncertainty with simulation-based inference
Mar 19, 2026A. A. Saoulis, T. -S. Pham, A. M. G. FerreiraSeismology Uncertainty Quantification
Department of Physics & Astronomy, University College London, Gower Street, London, WC1E 6BT, United Kingdom · Department of Earth Sciences, University College London, 5 Gower Place, London, WC1E 6BS, United Kingdom · Research School of Earth Sciences, The Australian National University, Canberra, ACT, Australia
Physics-Aware Machine Learning for Seismic and Volcanic Signal Interpretation
Mar 18, 2026William ThorossianPhysics-Informed ML Seismology
Large-Scale 3D Ground-Motion Synthesis with Physics-Inspired Latent Operator Flow Matching
Mar 18, 2026Yaozhong Shi, Grigorios Lavrentiadis, Konstantinos Tsalouchidis +5Physics-Informed Generative Modeling Seismology
Division of Engineering and Applied Sciences, California Institute of Technology, Pasadena, CA, USA · School of Engineering and Applied Sciences, University at Buffalo, Buffalo, NY, USA · Lawrence Berkeley National Laboratory, Berkeley, CA, USA +2
Domain-informed explainable boosting machines for trustworthy lateral spread predictions
Mar 17, 2026Cheng-Hsi Hsiao, Krishna Kumar, Ellen M. RathjeInterpretable ML Physics-Informed ML
GreenPhase: A Green Learning Approach for Earthquake Phase Picking
Feb 23, 2026Yixing Wu, Shiou-Ya Wang, Dingyi Nie +5Seismology Efficient Neural Network Inference
Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California · Center for Earth and Environmental Studies, National Central University · Department of Computer Science & Information Engineering, National Central University

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Evaluating PhaseNet on Teleseismic Data with MsPASS

Jinxin Ma, Yinzhi Wang, Gary L. Pavlis, Chenbo Yin

Texas Advanced Computing Center, The University of Texas at Austin · Department of Earth and Atmospheric Sciences, Indiana University, Bloomington, IN 47405

Efficient Neural Network Inference Seismology

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Numerous studies have shown that the machine-learning picker PhaseNet produces accurate P and S picks on local earthquake signals, but its performance can degrade sharply on teleseismic signals. To address this limitation, we present a reproducible MsPASS workflow that (i) enables scalable data preparation and management for large seismic archives and (ii) supports standardized PhaseNet training and inference. We assembled a control dataset of 1.6 million waveforms linked to teleseismic P-wave picks made by analysts at the USArray Array Network Facility (ANF). The control dataset confirms that the PhaseNet model trained on regional signals performs poorly on these data. We then trained PhaseNet from scratch on the training split of the ANF control dataset and evaluated it on a non-overlapping held-out test split, increasing P-pick recall by 741.5% and yielding 683.9% more picks within a 0.1s residual window. We also evaluated PhaseNet across different model sizes on both CPUs and GPUs. Increasing the model size by about 120 times improved precision and recall by 15.6% and 23.2%, respectively. However, the scaled model reduced inference throughput by 87.2% on an NVIDIA A100 GPU and by 97.3% on a 128-core high-performance CPU node. These results indicate that scaling PhaseNet is more practical on GPUs than on CPUs, and that simply enlarging the model is not an efficient way to achieve large accuracy gains.