126 citations · 204 across the 11 of their papers we have counts for
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Towards End-to-End Earthquake Monitoring Using a Multitask Deep Learning Model
Weiqiang Zhu, Junhao Song, Haoyu Wang +1
Seismic waveforms contain rich information about earthquake processes, making effective data analysis crucial for earthquake monitoring, source characterization, and seismic hazard…
A Global-scale Database of Seismic Phases from Cloud-based Picking at Petabyte Scale
Yiyu Ni, Marine A. Denolle, Amanda M. Thomas +6
We present the first global-scale database of 4.3 billion P- and S-wave picks extracted from 1.3 PB continuous seismic data via a cloud-native workflow. Using cloud computing servi…
Seismic swarms unveil the mechanisms driving shallow slow slip dynamics in the Copiapó ridge, Northern Chile
Jannes Münchmeyer, Diego Molina, Mathilde Radiguet +6
Like earthquakes, slow slip events release elastic energy stored on faults. Yet, the mechanisms behind slow slip instability and its relationship with seismicity are debated. Here,…
SeisLM: a Foundation Model for Seismic Waveforms
Tianlin Liu, Jannes Münchmeyer, Laura Laurenti +3
We introduce the Seismic Language Model (SeisLM), a foundational model designed to analyze seismic waveforms -- signals generated by Earth's vibrations such as the ones originating…
Deep learning detects uncataloged low-frequency earthquakes across regions
Jannes Münchmeyer, Sophie Giffard-Roisin, Marielle Malfante +4
Documenting the interplay between slow deformation and seismic ruptures is essential to understand the physics of earthquakes nucleation. However, slow deformation is often difficu…
PyOcto: A high-throughput seismic phase associator
Jannes Münchmeyer
Seismic phase association is an essential task for characterising seismicity: given a collection of phase picks, identify all seismic events in the data. In recent years, machine l…