Monitoring of seismic signals generated by slow deformation at convergent and transform plate boundaries worldwide, known as tectonic tremor, might provide insights into deformation processes in the source regions of megathrust earthquakes. Tremor signals occur dominantly in the 2–8 Hz frequency band and can last for tens of seconds to several minutes, in contrast to typical earthquakes that produce seismic signals at frequencies up to several tens of hertz and last less than a minute. Because tremor is caused by stochastic processes, the resultant waveforms are represented by a stochastic function and construction of deterministic measures to discriminate tremor signals from earthquakes is very difficult. In this study, we used a convolutional neural network (CNN) to discriminate the signals of tectonic tremor from those of local earthquakes in running spectral images of these signals. We developed a method (seismic running spectra‐CNN [SRSpec‐CNN]) that is sensitive to the absolute frequency of signal appearance, which reflects the physical properties of the signal source, but is insensitive to the time of signal onset. SRSpec‐CNN has 130,211 parameters that were trained by 17,213 images of 64×64  pixels. Based on simultaneous analyses of the frequency contents and durations of the signals, we achieved 99.5% accuracy for our identifications of signals from tectonic tremor, local earthquakes, and noise. Because running spectra clearly differentiate the characteristic features of these signals, we were able to achieve this high accuracy by using a CNN of simple architecture.

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