It is of great significance to accurately pick up the first arrival of P wave and S wave for the accurate location of earthquakes and the explanation of seismogenic mechanism. This paper focuses on the analysis of seismic distribution and seismic wave velocity of the bottom shell based on machine learning. In this paper, convolutional neural network is used to pick up seismic P and S waves when they first arrive. Compared with the traditional STA/LTA, the convolutional neural network method does not manually set thresholds and manually select feature functions, but only relies on convolutional neural network to automatically extract waveform features, and the model has good generalization. The research results of this paper can provide a new idea for picking up P and S waves at their first arrival in the future, so as to pick up P and S waves at their first arrival more accurately, and it is expected to provide technical support for the location of earthquakes and the explanation of seismogenic mechanism of earthquakes.

Rashid Kim. Earthquake Distribution and Crustal Seismic Wave Velocity Based on Machine Learning. Machine Learning Theory and Practice (2020), Vol. 1, Issue 3: 37-44. https://doi.org/10.38007/ML.2020.010305.

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