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.
翻译:地震的探测是地震学的基本先决条件,有助于各种研究领域,例如预测地震和了解地壳/地壳结构。机器学习技术的最近进展使得能够从波形数据自动探测地震。特别是,对这项工作采用了各种最先进的深层学习方法。在这项研究中,我们提出并试验了一个采用深层学习的新阶段探测方法,该方法基于一个新的框架的标准共振神经网络。拟议方法的新颖性是其单独明确的全球和局部波形表现学习战略,这增强了其稳健性和灵活性。在模拟拟议方法之前,我们通过多组波形数据对波形进行自动探测。根据这一结果,我们考虑了全球代表性和两种波形的局部表现。随后,对每个全球和地方代表进行了不同的阶段探测模型进行了培训。对于新的波状,整个阶段的概率被评估为每个模型阶段的稳定性。在模拟方法之前,通过多组组合对波形进行进一步的波形表示,在对波形图进行多组数据进行最佳分解。根据这一方法,我们提出了一种对地震测得稳妥性数据进行演示的方法。最后,通过演示测测测测测测测测地震的方法,以其他测测测测测测地震的方法。对地震数据进行。