Automatic Waveform Classification and Arrival Picking Based on Convolutional Neural Network. Issue 7 (25th July 2019)

• Record Type: Journal Article
• Authors: Chen, Yangkang; Zhang, Guoyin; Bai, Min; Zu, Shaohuan; Guan, Zhe; Zhang, Mi
• Is Part Of: Earth and space science. Volume 6:Issue 7(2019)
• Page Start: 1244
• Page End: 1261
• Publication Date: 2019-07-25
• Subjects: deep learning -- arrival picking -- microseismic; Space sciences -- Periodicals; Geophysics -- Periodicals; 500.5
• DOI: 10.1029/2018EA000466
• Languages: English
• ISSNs: 2333-5084

Abstract

Abstract : Automatic waveform classification and arrival picking methods are widely studied to reduce or replace the manual works. Machine learning based methods, especially neural networks, and clustering based methods have shown great potentials in previous studies. However, most of the existing methods are sensitive to noise. The convolution neural networks (CNNs), developed from the traditional neural networks, have been successfully applied in many different fields, but are rarely studied in seismic waveform classification. In this paper, we propose a novel antinoise CNN architecture for waveform classification and also propose to combine k‐means clustering (KC) with CNN classification to pick arrivals (CNN‐KC). Seismic data are sampled to 1‐D vectors using a specific time window. Using the trained CNN classifier, these 1‐D vectors are classified into two categories: waveform and nonwaveform. With the constraint of the first waveform, CNN‐KC can pick the arrival more accurately. We also apply the proposed methods to the synthetic microseismic data with different noise levels and the actual field microseismic data to test their robustness. CNNs perform much better than the traditional multilayer perceptron on the waveform classification of the noisy microseismic data. Based on the analysis of the CNN internal architecture, we also conclude that the main reason that CNN is insensitive to noise is the convolution and pooling layers which behave like smooth operation in some ways. The final results show that the CNN and CNN‐KC are effective and robust methods for waveform classification and arrival picking. Key Points: We introduce the concept of waveform classification, by which we can automatically pick the arrivals and potentially reject noise We propose a novel CNN architecture for waveform classification and also combine k‐means clustering with CNN to pick arrivals CNNs perform much better than the traditional multilayer perceptron (MLP) on the waveform classification of the noisy microseismic data