Machine Learning in Seismology: Turning Data into Insights

By Qingkai Kong, D. Trugman, Zachary E. Ross, Michael J. Bianco, Brendan J. Meade, Peter Gerstoft. Seismological Research Letters, 2018

433 citations recorded by the source corpus.

This article provides an overview of current applications of machine learning (ML) in seismology. ML techniques are becoming increasingly widespread in seismology, with applications ranging from identifying unseen signals and patterns to extracting features that might improve our physical understanding. The survey of the applications in seismology presented here serves as a catalyst for further use of ML. Five research areas in seismology are surveyed in which ML classification, regression, clustering algorithms show promise: earthquake detection and phase picking, earthquake early warning (EEW), ground‐motion prediction, seismic tomography, and earthquake geodesy. We conclude by discussing the need for a hybrid approach combining data‐driven ML with traditional physical modeling.

Subfields: Artificial Intelligence, Geophysics

View Paper: https://www.semanticscholar.org/paper/e566ffd4e441d6d43ffb8012153e71a395098d6a

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Citations in the corpus

The promise of implementing machine learning in earthquake engineering: A state-of-the-art review (2020, methodology): “Figure 1 summarizes the two types of ML and some commonly used ML algorithms (Kong et al., 2018).”
Machine learning in acoustics: Theory and applications. (2019, background): “11,12 ML in acoustics is a rapidly developing ﬁeld, with many compelling solutions to the aforementioned acoustics challenges.”
Physics‐Informed Neural Networks (PINNs) for Wave Propagation and Full Waveform Inversions (2021, background): “…explosion of recorded seismic data made available over the past decade, seismologists have begun to search for modern efficient techniques (Kong et al., 2019; Bergen et al., 2019) to tackle problems such as earthquake detection, automatic phase picking (Mousavi et al., 2020; Ross et al.,…”
Which Picker Fits My Data? A Quantitative Evaluation of Deep Learning Based Seismic Pickers (2021, background): “Due to the well-defined nature of these tasks they represent one of the first fields of application of neural networks in seismology (Bergen et al., 2019; Kong et al., 2019).”