NT-Com: A combined machine learning model for picking up first arrival

We present a combined model named NT-Com that could predict the noise of observation site in China and effectively pick up first arrivals with very high accuracy. Based on a nonlinear autoregressive exogenous model (NARX) known as a variant of recurrent neural network and classification-regression trees (CART), NT-Com picks first arrivals by detecting abrupt points in time series. Several experiments were performed to check the fitting degree and prediction accuracy of NARX on noise signals, and experiments on CART were made to measure the accuracy of first break picking. The experimental results of NT-Com are close to those of experts and better than the traditional automatic detection algorithms. Moreover, the accuracy of NT-Com can reach 96%. The time error of the P-wave first arrival pickup can be shortened to 0.5 s. Because various first arrival patterns are learned during the training process, they can be effectively identified, which leads to a much lower probability of false triggers than traditional automatic detection algorithms. Trained and tested by all seismic data in China Earthquake networks from 2009-2019, the research work finds out a solution for picking up first arrivals by combining a simple structure of the neural networks and trees, which can be applied in China with its strong generalization ability and robustness.

Automated summary

We propose NT-Com, a combined model that predicts noise in Chinese observation sites and accurately picks up first arrivals. By combining a nonlinear autoregressive exogenous model (NARX) and classification-regression trees (CART), NT-Com detects abrupt points to identify first arrivals with high accuracy. Experimental results show that NT-Com outperforms traditional automatic detection algorithms and achieves an accuracy of 96% with a time error of 0.5 seconds for P-wave first arrival pickup. The model's ability to effectively identify various first arrival patterns and its robustness make it applicable in China.