Thread: 2022 AGU session S014 - Machine-Learning-Based Earthquake Monitoring and Seismic Analysis

Started: 2022-07-26 18:49:26
Last activity: 2022-07-26 18:49:26
Topics: AGU Meetings
Dear Colleagues,

We would like to draw your attention to the following session at the 2022 AGU Fall Meeting:

S014 - Machine-Learning-Based Earthquake Monitoring and Seismic Analysis

Session description:
In recent years, machine learning (ML) techniques have been widely applied to earthquake monitoring and seismic analysis including seismic detection, seismic classification, seismic denoising, phase picking, phase association, earthquake location, magnitude estimation, ground motion prediction, earthquake early warning, source inversion, and subsurface imaging. ML-based phase picking has attracted much attention due to its remarkable performance in terms of accuracy and efficiency, and has been widely adopted for earthquake monitoring at local and regional scales, while global and regional (> 150 km) ML phase pickers have seen less attention. On the other hand, advances have also been made on waveform-based ML location algorithms, which can directly infer earthquake location information from continuous seismograms with the potential for near-real-time earthquake early warning. For these techniques, there is still room to improve the location accuracy and generalization capabilities, as well as for effectively accounting for variable seismic network geometry. ML-based first-motion polarity determination methods have shown promise in earthquake focal mechanism inversion, but full-waveform-based ML algorithms for focal mechanism inversion have still seen minimal attention. In addition, few ML-based methods are available to invert earthquake rupture processes from point sources to finite fault models. ML-based seismic tomography and imaging methods are also needed to better understand seismogenic structures. This session aims to highlight new ML methods for earthquake monitoring and seismic analysis and their applications in broad aspects of seismological studies.

This meeting will be in a hybrid format for both in-person and online participation. The abstract deadline is August 3, 2022. Please submit your abstract at

We look forward to your contributions to our session!

Best wishes from session conveners,
Miao Zhang (Dalhousie University)
Weiqiang Zhu (California Institute of Technology)
Ian W. McBrearty (Stanford University)
Jannes Münchmeyer (Helmholtz Centre Potsdam GFZ)
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