[S-TT37] Seismic Big Data Analysis Based on the State-of-the-Art of Bayesian Statistics

[STT37-P02] Convolutional Neural Network to Detect Deep Low-Frequency Tremors from Seismic Waveform Images

*Ryosuke Kaneko^1,2, Hiromichi Nagao^2,1, Shin-ichi Ito^2,1, Kazushige Obara², Hiroshi Tsuruoka² (1.Graduate School of Information Science and Technology, The University of Tokyo, 2.Earthquake Research Institute, The University of Tokyo)

Keywords:Deep low-frequency tremor, Convolutional neural network, ResNet, Grad-CAM

The establishment of Hi-net [1, 2] led to the discovery of deep low-frequency tremors [3]. Considering the expected relations between tremors and large earthquakes (e.g., [4]), it is important to investigate tremors that occurred before establishing the dense seismometer arrays. Past seismometers used more than 50 years ago drew waveforms continuously on paper wrapped on a drum. The digitization of seismograph paper records by tracing the waveforms is effective for investigating large earthquakes because such waveforms are distinctive and consequently extractable given the low frequencies and large amplitudes. In contrast, tremors have much smaller amplitudes and higher frequencies than large earthquakes, so their digitization is much more difficult due to overlapping waveforms. Therefore, we use a convolutional neural network (CNN) aiming to detect evidence of tremors from seismograph paper records. The CNN is a representative deep learning method that exhibits high performance in tasks such as image recognition and handwriting recognition. A CNN can automatically tune its internal parameters by learning the characteristics of tremors from input images without requiring prior knowledge of tremors or manually adjusting the parameters. Training a CNN from scratch with real data polluted by a variety of noises may hinder the model construction and hyperparameter tuning. Thus, we conducted numerical experiments to train a CNN with synthetic images generated according to seismograph paper records. We constructed the CNN based on the ResNet [5] architecture for better performance. The results show that the trained model can learn tremor features and correctly determine the presence of tremors in the seismic waveforms. In addition, heatmaps generated based on gradient-weighted class activation mapping (Grad-CAM) [6] clearly indicate the tremor location on each image. These suggest that a CNN can be a promising alternative for effective tremor detection compared to individual waveform extraction through digitization. Based on the finding from the experiments, we will conduct CNN training with real data and apply the trained model to seismograph paper records.

References
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