以JOPENS系统实时流接收为基础，应用Redis共享内存技术和近年来发展较快的深度学习震相自动识别技术，设计一套可7×24小时不间断稳定接收并实时识别连续地震流数据中P、S震相的系统，为地震台网实时数据处理提供一套辅助工具，并在福建省地震局测震台网128个台站的实时数据流上进行测试。该工具由Redis实时数据流共享模块与深度学习震相到时自动拾取、MSDP震相格式转换3个模块组成，可以实时接收并自动识别台网地震连续波形，生成P、S震相报告，并可导入MSDP人机交互工具进一步处理，在一定程度上可以减轻人工处理工作量。
We provided a set of auxiliary tools for real-time stream data processing of the seismic networks. The tool consists of three modules:the Redis real-time data stream sharing module, the automatic phase picking module by deep learning, and the phase format conversion tool for MSDP. This tool has been tested on the real-time data streams of 128 stations in the Seismic Network of Fujian Province. Its biggest advantage is that it can receive continuous waveforms of the network in real-time and automatically identify and generate P and S phase reports. Phase reports can also be imported into MSDP human-computer interaction software for further processing, which can reduce the manual processing workload to a certain extent.
2020,41(2): 165-171 收稿日期：2020-03-06
DOI：10.3969/j.issn.1003-3246.2020.02.019
基金项目：中国地震局地球物理研究所基本科研业务专项（项目编号：DQJB19A0114）；中国地震局监测预报司自动编目专项；国家自然科学基金（项目编号：41804047）
作者简介：赵明(1984-05-),中国地震局地球物理研究所助理研究员,主要从事地震学和机器学习方法研究工作。E-mail:mzhao@cea-igp.ac.cn
参考文献：
吴永权,黄文辉. 数据处理系统软件JOPENS的架构设计与实现[J]. 地震地磁观测与研究,2010,31(6):59-63.
赵明,陈石,Yuen D. 基于深度学习卷积神经网络的地震波形自动分类与识别[J]. 地球物理学报,2019a,62(1):374-382.
赵明,房立华,陈石,Yuen D A. 基于U形卷积神经网络的震相识别与到时拾取方法研究[J]. 地球物理学报,2019b,62(8):3034-3042.
Allen R. Automatic phase pickers:Their present use and future prospects[J]. Bulletin of the Seismological Society of America, 1982, 72(6B):S225-S242.
Liu M, Zhang M, Zhu W Q, et al. Rapid characterization of the July 2019 Ridgecrest, California, earthquake sequence from raw seismic data using machine-learning phase picker[J]. Geophysical Research Letters, 2020, 47(4):e2019GL086189.
Ross Z E, Meier M A, Hauksson E, Heaton T H. Generalized seismic phase detection with deep learning[J]. Bull Seismol Soc Am, 2018, 108(5A):2894-2901, doi:10.1785/0120180080.
Sleeman R, Van Eck T. Robust automatic P-phase picking:an on-line implementation in the analysis of broadband seismogram recordings[J]. Physics of the Earth and Planetary Interiors, 1999, 113(1/2/3/4):265-275.
Zhu W Q, Beroza G C. Phasenet:a deep-neural-network-based seismic arrival-time picking method[J]. Geophysical Journal International, 2019, 216(1):261-273.
