This article gives an overview of machine learning (ML) applications in MyShake-a crowdsourcing global smartphone seismic network. Algorithms from classification, regression, and clustering are used in the MyShake system to address various problems, such as artificial neural network (ANN) and convolutional neural network (CNN) to distinguish earthquake motions, spatial-temporal clustering using density-based spatial clustering of applications with noise (DBSCAN) to detect earthquakes from phone aggregated information, and random forest regression to learn from existing physics-based relationships. Beyond existing efforts, this article also presents a vision of the role of ML in some new directions and challenges. Using MyShake as an example, this article demonstrates the promising combination of ML and seismology.
REF described the machine learning algorithms used in the MyShake system.
Machine Learning Aspects of the MyShake Global Smartphone Seismic Network
Seismological Research Letters
Geology