Seismic-phase detection using multiple deep learning models for global and local representations of waveforms

• URL: http://arxiv.org/abs/2211.02261v1
• Date: Fri, 4 Nov 2022 04:37:04 GMT
• Title: Seismic-phase detection using multiple deep learning models for global and local representations of waveforms
• Authors: Tomoki Tokuda and Hiromichi Nagao
• Abstract summary: Recent advances in machine learning have enabled the automatic detection of earthquakes from waveform data. In this study, we proposed and tested a novel phase detection method employing deep learning. The novelty of the proposed method is its separate explicit learning strategy for global and local representations of waveforms.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The detection of earthquakes is a fundamental prerequisite for seismology and contributes to various research areas, such as forecasting earthquakes and understanding the crust/mantle structure. Recent advances in machine learning technologies have enabled the automatic detection of earthquakes from waveform data. In particular, various state-of-the-art deep-learning methods have been applied to this endeavour. In this study, we proposed and tested a novel phase detection method employing deep learning, which is based on a standard convolutional neural network in a new framework. The novelty of the proposed method is its separate explicit learning strategy for global and local representations of waveforms, which enhances its robustness and flexibility. Prior to modelling the proposed method, we identified local representations of the waveform by the multiple clustering of waveforms, in which the data points were optimally partitioned. Based on this result, we considered a global representation and two local representations of the waveform. Subsequently, different phase detection models were trained for each global and local representation. For a new waveform, the overall phase probability was evaluated as a product of the phase probabilities of each model. This additional information on local representations makes the proposed method robust to noise, which is demonstrated by its application to the test data. Furthermore, an application to seismic swarm data demonstrated the robust performance of the proposed method compared with those of other deep learning methods. Finally, in an application to low-frequency earthquakes, we demonstrated the flexibility of the proposed method, which is readily adaptable for the detection of low-frequency earthquakes by retraining only a local model.

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