Seismology and Earthquake Studies

Neueste Paper

• Fact Grounded Attention: Eliminating Hallucination in Large Language Models Through Attention Level Knowledge Integration

  Aayush Gupta, Manish Choudhary · 21. September 2026

  "The greatest enemy of knowledge is not ignorance, it is the illusion of knowledge." Large Language Models have conquered natural language but remain prisoners of their own probabilistic nature--confidently hallucinating facts they never truly knew. We present Fact Grounded Attention (FGA), a novel …

• Seismic Site Response Prediction from Sparse Observations Using Finite-Element-Pretrained Latent Dynamics

  Yi Zhu, Su Chen, Xiaojun Li · 18. September 2026

  Numerical site-response predictions often deviate from observations, yet correcting these discrepancies is difficult because records are limited in both sensor coverage and number of events. This study proposes the Transfer-Enabled Forced Latent Autoencoder for Response Equations (FLARE-T) to improv…

• SeisMamba: Low-Latency Single-Station Seismic Magnitude Estimation for Spatially Distributed Earthquake Early Warning

  Quenton Yeo, Zhaoge Bi, Linghan Huang, Luke Stephen Higgins, Flora Salim, Huaming Chen · 26. August 2026

  Rapid earthquake magnitude estimation is central to earthquake early warning, yet many operational systems depend on dense regional seismic networks and region-specific calibration. This creates a spatial coverage barrier for high-risk areas with sparse sensing infrastructure. Single-station learnin…

• LEMMA-RCA: A Large Multi-modal Multi-domain Dataset for Root Cause Analysis

  Lecheng Zheng, Zhengzhang Chen, Dongjie Wang, Chengyuan Deng, Reon Matsuoka, Haifeng Chen · 26. August 2026

  Root cause analysis (RCA) is crucial for enhancing the reliability and performance of complex systems. However, progress in this field has been hindered by the lack of large-scale, open-source datasets tailored for RCA. To bridge this gap, we introduce LEMMA-RCA, a large dataset designed for diverse…

• Consensus-gated Multi-Agent Neural Architecture Search for Seismic Fault Segmentation

  Shehram Baig, Ahmad Mustafa · 17. August 2026

  Neural networks for seismic fault segmentation are often borrowed from computer vision and medical imaging domains where they train under relatively much larger labeled data resources. Optimizing their architecture under tight labeled data budgets as are common in geophysical applications is not a t…

• Automatic Knowledge Graph Construction and Query for Earthquake Catalogs

  Yuxin Zhou, Huai Zhang, S. Mostafa Mousavi · 29. Juli 2026

  In recent years, the number of events in earthquake catalogs has significantly increased due to the utilization of more effective deep learning based detectors and phase pickers but answering open ended questions such as what characterizes this sequence? remains constrained by rigid spatiotemporal w…

• An Agentic Interface for End-to-End Probabilistic Seismic Hazard and Risk Analysis

  Sreenath Vemula, Pierre Jehel, Fabrice Cotton, Filippo Gatti · 21. Juli 2026

  Probabilistic seismic hazard and risk analyses are backbone to building codes, insurance pricing, and disaster management. Yet their open-engine pipelines remain accessible primarily to experts. We present the first agentic interface to the end-to-end probabilistic seismic hazard and risk chain via …

• HASTE: A Platform for Rapid Post-Disaster Building Damage Assessment

  Caleb Robinson, Anthony Ortiz, Simone Fobi Nsutezo, Cameron Birge, Meygha Machado, Marcelo Duarte, Joaquin Rivero Rodriguez, Anthony Cintron Roman, Kevin White, Inbal Becker-Reshef, Juan M. Lavista Ferres · 14. Juli 2026

  When a large disaster strikes, responders need a map of which buildings are damaged within hours. The models that do well on public benchmarks assume matched before-and-after imagery and a training set drawn from similar past events, and neither is usually available for a new disaster in its first d…

• Two kinds of robustness are not the same: disentangling fault tolerance and low-SNR robustness in multi-domain event detection on real data

  Isao Kurosawa · 30. Juni 2026

  Reliable event detection underpins induced-seismicity monitoring for Carbon dioxide Capture and Storage (CCS) and geothermal operations, distributed acoustic sensing (DAS), and industrial condition monitoring. In each setting a detector must stay reliable both when sensors fail and when the signal i…

• An Integrated Two-Stage Deep-Learning Tool for Rapid Post-Hurricane Damage Identification and Repair Scheduling

  Hooman Torkaman, Ellis Oti Boateng, Jignesh Solanki, Anurag Srivastava · 30. Juni 2026

  Post-hurricane damage assessment and repair scheduling can require computationally intensive simulation and optimization. This paper presents an integrated two-stage deep-learning tool for rapid damaged-line identification and repair-schedule computation. An available offline synthetic dataset for t…

• Bridging Data Gaps in Structural Fragility Modeling through Transfer Learning: Methodology and Case Studies

  Narges Saeednejad, Jamie Ellen Padgett · 18. Juni 2026

  This paper presents a methodology-centered transfer learning framework for fragility adaptation under domain shift, class imbalance, and scarce target labels while preserving engineering interpretability and supporting decision-making under uncertainty. Four transfer learning strategies (instance-ba…

• Learning Earthquake Wave Arrival Time Picking from Labels with Inaccuracies

  Sen Li, Xu Yang, S. Mostafa Mousavi, Anye Cao, Keting Fan, Yaoqi Liu, Changbin Wang, Qiang Niu · 16. Juni 2026

  Inaccurately labeled training data, or "label noise", poses a significant threat to the integrity of supervised machine learning models. This corruption directly degrades performance by teaching the model erroneous mappings between features and labels, which leads to poor generalization and reduced …

• When Do Autoregressive Sequence Models Forecast Physical Wavefields? A Controlled Study on Synthetic Seismograms

  Waleed Esmail, Stuart Russell, Jana Klinge, Alexander Kappes, Christine Thomas · 10. Juni 2026

  Long-horizon autoregressive forecasting of oscillatory physical signals, such as seismograms, gravitational-wave strain, and similar wavefields is limited by error accumulation: as a causal model is fed its own outputs over hundreds of steps, small per-step errors compound into phase drift that poin…

• Spatiotemporal Seismic Hazard Assessment Using VQ-VAE and Seismic Statistical Features

  Wei Quan, Denise Gorse · 10. Juni 2026

  In this paper we build upon a previous study in which we demonstrated, using XGBoost and earthquake catalogue data from Japan and Chile, that a set of 60 seismic statistical features (SSFs) had much greater predictive value than a set of 428 generic time series features from the tsfresh package. We …

• Data-Driven Forecasting of three-Component Seismograms Using Transformer Architectures

  Waleed Esmail, Stuart Russell, Jana Klinge, Alexander Kappes, Christine Thomas · 3. Juni 2026

  Forecasting seismic waveforms beyond observed data remains challenging due to the nonlinear, dispersive, and multi-scale nature of seismic wave propagation. In this work, we introduce \textsc{SeismoGPT}, a transformer-based autoregressive model designed to forecast three-component seismic waveforms …

• Revisiting Change Detection Methods for their Application to Serac Fall Time-Lapse Monitoring

  Arthur D\'er\'edel, Carlos Crispim-Junior, Pierre Lemaire, Johan Berthet, Laure Tougne Rodet · 28. Mai 2026

  In an era where climate change aggravates environmental uncertainties, the identification and detection of event precursors are becoming crucial to mitigate the impacts of disastrous natural hazards. While classical sensors such as interferometric lasers or seismometers are reliable, their widesprea…

• MULTISEISMO: A Multimodal Seismic Dataset and Model for Cross-Modal Seismic Understanding

  Sai Munikoti, Ian Stewart, Chengping Chai, Lisa Linville, Scott Vasquez, Sameera Horawalavithana, Karl Pazdernik · 27. Mai 2026

  The application of generalist multimodal models (GMMs) to specialized scientific domains remains limited due to the scarcity of comprehensive domain-specific datasets that integrate multiple data modalities beyond text and images. In seismology, understanding earthquake phenomena requires the synthe…

• Evaluating PhaseNet on Teleseismic Data with MsPASS

  Jinxin Ma, Yinzhi Wang, Gary L. Pavlis, Chenbo Yin · 25. Mai 2026

  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 prep…

• Real-Time Earthquake Magnitude Classification from Initial P-Waves: Models, Dataset, and Comparative Analysis for South Asia

  Md Nasiat Hasan Fahim, Md. Abid Ullah Muhib, Rayhanul Amin Tanvir, Abdullah Al Noman · 25. Mai 2026

  Rapid earthquake magnitude estimation is crucial for effective early warning systems that can save lives and reduce economic damage. In this paper, we present a comprehensive study of magnitude classification using only the vertical component of the initial 7-second P-wave window from a single stati…

• Neural Negative Binomial Regression for Weekly Seismicity Forecasting: Per-Cell Dispersion Estimation and Tail Risk Assessment

  Alim Igilik · 21. Mai 2026

  Standard approaches to forecasting the weekly number of earthquakes on a spatial grid rely on the Poisson distribution with a single global dispersion assumption. We show that this assumption is systematically violated in seismic data from Central Asia (2010-2024), where a likelihood-ratio test with…

• QuChaTeR: A Hybrid Quantum-Chaotic Temporal Framework for Earthquake Prediction

  Emir Kaan \"Ozdemir · 19. Mai 2026

  Seismic prediction remains challenging due to the highly nonlinear and chaotic dynamics of earthquake signals. While classical deep learning models such as LSTMs and CNNs capture local temporal features, and quantum models offer richer state representations, their integration with chaos-driven mecha…

• Sensoformer: Robust Sim-to-Real Inference on Variable-Geometry Sensor Sets via Physics-Structured Randomization

  Zhe Jia, Xiaotian Zhang, Junpeng Li · 8. Mai 2026

  Inferring high-dimensional physical states from sparse, ad-hoc sensor arrays is a fundamental challenge across AI for Science and industrial IoT. Standard machine learning architectures struggle in these domains due to irregular, variable-cardinality sensor geometries and the profound sim-to-real di…

• Thermal Anomaly Detection using Physics Aware Neuromorphic Networks: Comparison between Raw and L1C Sentinel-2 Data

  Stephen Smith, Cormac Purcell, Gabriele Meoni, Roberto Del Prete, Zdenka Kuncic · 22. April 2026

  Damage caused by bushfires and volcanic eruptions escalates rapidly when detection is delayed, making fast and reliable early warning capabilities essential. Recent Earth Observation (EO) approaches have shown that thermal anomaly detection can be performed directly on decompressed Level-0 (L0) sens…

• Recovering Sub-threshold S-wave Arrivals in Deep Learning Phase Pickers via Shape-Aware Loss

  Chun-Ming Huang, Li-Heng Chang, I-Hsin Chang, An-Sheng Lee, Hao Kuo-Chen · 6. April 2026

  Deep learning has transformed seismic phase picking, but a systematic failure mode persists: for some S-wave arrivals that appear unambiguous to human analysts, the model produces only a distorted peak trapped below the detection threshold, even as the P-wave prediction on the same record appears fl…

• Leveraging LLMs and Social Media to Understand User Perception of Smartphone-Based Earthquake Early Warnings

  Hanjing Wang, S. Mostafa Mousavi, Patrick Robertson, Richard M. Allen, Alexie Barski, Robert Bosch, Nivetha Thiruverahan, Youngmin Cho, Tajinder Gadh, Steve Malkos, Boone Spooner, Greg Wimpey, Marc Stogaitis · 25. März 2026

  Android's Earthquake Alert (AEA) system provided timely early warnings to millions during the Mw 6.2 Marmara Ereglisi, T\"urkiye earthquake on April 23, 2025. This event, the largest in the region in 25 years, served as a critical real-world test for smartphone-based Earthquake Early Warning (EEW) s…