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Active Source Seismic Identification and Automatic Picking of the P-wave First Arrival Using a Convolutional Neural Network
XU Zhen1, WANG Tao1, XU Shanhui2, WANG Baoshan2,3, FENG Xuping1, SHI Jing1, YANG Minghan1
1.Institute of Earth Exploration and Sensing(IEES), School of Earth Sciences and Engineering, Nanjing University, Nanjing 210046, China;2.Institute of Geophysics, China Earthquake Administration, Beijing 100081, China;3.School of Earth and Space Sciences, University of Science and Technology of China, Hefei 230026, China
摘要:
In seismic data processing, picking of the P-wave first arrivals takes up plenty of time and labor, and its accuracy plays a key role in imaging seismic structures. Based on the convolution neural network (CNN), we propose a new method to pick up the P-wave first arrivals automatically. Emitted from MINI28 vibroseis in the Jingdezhen seismic experiment, the vertical component of seismic waveforms recorded by EPS 32-bit portable seismometers are used for manually picking up the first arrivals (a total of 7242). Based on these arrivals, we establish the training and testing sets, including 25,290 event samples and 710,616 noise samples (length of each sample:2s). After 3,000 steps of training, we obtain a convergent CNN model, which can automatically classify seismic events and noise samples with high accuracy (> 99%). With the trained CNN model, we scan continuous seismic records and take the maximum output (probability of a seismic event) as the P-wave first arrival time. Compared with STA/LTA (short time average/long time average), our method shows higher precision and stronger anti-noise ability, especially with the low SNR seismic data. This CNN method is of great significance for promoting the intellectualization of seismic data processing, improving the resolution of seismic imaging, and promoting the joint inversion of active and passive sources.
关键词:  CNN  Active source seismic identification  First arrival picking  Anti-noise ability
DOI:10.19743/j.cnki.0891-4176.201902014
分类号:
基金项目:This project is sponsored by the National Key Research and Development Project (2018YFC1503202-01) and the Emergency Management Project of the National Natural Science Foundation of China(41842042).
Active Source Seismic Identification and Automatic Picking of the P-wave First Arrival Using a Convolutional Neural Network
XU Zhen1, WANG Tao1, XU Shanhui2, WANG Baoshan2,3, FENG Xuping1, SHI Jing1, YANG Minghan1
1.Institute of Earth Exploration and Sensing(IEES), School of Earth Sciences and Engineering, Nanjing University, Nanjing 210046, China;2.Institute of Geophysics, China Earthquake Administration, Beijing 100081, China;3.School of Earth and Space Sciences, University of Science and Technology of China, Hefei 230026, China
Abstract:
In seismic data processing, picking of the P-wave first arrivals takes up plenty of time and labor, and its accuracy plays a key role in imaging seismic structures. Based on the convolution neural network (CNN), we propose a new method to pick up the P-wave first arrivals automatically. Emitted from MINI28 vibroseis in the Jingdezhen seismic experiment, the vertical component of seismic waveforms recorded by EPS 32-bit portable seismometers are used for manually picking up the first arrivals (a total of 7242). Based on these arrivals, we establish the training and testing sets, including 25,290 event samples and 710,616 noise samples (length of each sample:2s). After 3,000 steps of training, we obtain a convergent CNN model, which can automatically classify seismic events and noise samples with high accuracy (> 99%). With the trained CNN model, we scan continuous seismic records and take the maximum output (probability of a seismic event) as the P-wave first arrival time. Compared with STA/LTA (short time average/long time average), our method shows higher precision and stronger anti-noise ability, especially with the low SNR seismic data. This CNN method is of great significance for promoting the intellectualization of seismic data processing, improving the resolution of seismic imaging, and promoting the joint inversion of active and passive sources.
Key words:  CNN  Active source seismic identification  First arrival picking  Anti-noise ability