552 citations · 1.4k across the 7 of their papers we have counts for
8 papers
Broadband Ground-Motion Synthesis by Conditioning Denoising Diffusion Models on Low-Frequency Waveforms
Kadek Hendrawan Palgunadi, Simon Dirmeier, Maria Koroni +2
Seismic hazard and structural analyses require broadband ground-motion time histories, yet the high-frequency band (above 1~Hz) is the hardest to obtain: physics-based simulations…
High Resolution Seismic Waveform Generation using Denoising Diffusion
Kadek Hendrawan Palgunadi, Andreas Bergmeister, Andrea Bosisio +5
Accurate prediction and synthesis of seismic waveforms are crucial for seismic-hazard assessment and earthquake-resistant infrastructure design. Existing prediction methods, such a…
SAIPy: A Python Package for single station Earthquake Monitoring using Deep Learning
Wei Li, Megha Chakraborty, Claudia Quinteros Cartaya +4
Seismology has witnessed significant advancements in recent years with the application of deep learning methods to address a broad range of problems. These techniques have demonstr…
Data-driven Accelerogram Synthesis using Deep Generative Models
Manuel A. Florez, Michaelangelo Caporale, Pakpoom Buabthong +3
Robust estimation of ground motions generated by scenario earthquakes is critical for many engineering applications. We leverage recent advances in Generative Adversarial Networks…
Reliable Real-time Seismic Signal/Noise Discrimination with Machine Learning
Men-Andrin Meier, Zachary E. Ross, Anshul Ramachandran +7
In Earthquake Early Warning (EEW), every sufficiently impulsive signal is potentially the first evidence for an unfolding large earthquake. More often than not, however, impulsive…
PhaseLink: A Deep Learning Approach to Seismic Phase Association
Zachary E. Ross, Yisong Yue, Men-Andrin Meier +2
Seismic phase association is a fundamental task in seismology that pertains to linking together phase detections on different sensors that originate from a common earthquake. It is…