Deep Learning-based Small Magnitude Earthquake Detection and Seismic Phase Classification

Reliable earthquake detection and seismic phase classification is often challenging especially in the circumstances of low magnitude events or poor signal-to-noise ratio. With improved seismometers and better global coverage, a sharp increase in the volume of recorded seismic data is witnessed. This makes the handling of the seismic data rather daunting based on traditional approaches and therefore fuels the need for a more robust and reliable method. In this study, we investigate two deep learning-basedmodels,termed1DResidualNeuralNetwork(ResNet)andmulti-branchResNet,fortacklingtheproblemofseismicsignaldetectionandphaseidentification,especiallythelatercanbeusedinthecasewheremultipleclassesisorganizedinthehierarchicalformat.ThesemethodsaretrainedandtestedonthedatasetoftheSouthernCaliforniaSeismicNetwork.Resultsdemonstratethattheproposedmethodscanachieverobustperformanceforthedetectionofseismicsignals,andtheidentificationofseismicphases,evenwhentheseismiceventsareofsmallmagnitudeandaremaskedbynoise.Comparedwithpreviouslyproposeddeeplearningmethods,theintroducedframeworksachieve4 % improvement in earthquake monitoring, and a slight enhancement in seismic phase classification.