基于深度学习技术的地震实时流数据自动处理辅助工具开发  

Development of auxiliary tools for automatic processing of seismic real-time stream data based on deep learning technology

作　　者：赵明[1] 廖诗荣[2] 黄玲珠[2] ZHAO Ming;LIAO Shirong;HUANG Lingzhu(Institute of Geophysics,China Earthquake Administration,Beijing 100081,China;Monitoring Center,Fujian Earthquake Agency,Fuzhou 350003,China)

机构地区：[1]中国地震局地球物理研究所,北京100081 [2]福建省地震局监测中心,福州350003

出　　处：《地震地磁观测与研究》2020年第2期165-171,共7页Seismological and Geomagnetic Observation and Research

基　　金：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)。

摘　　要：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.

关 键 词：深度学习 实时流 震相挑取 

分 类 号：P315[天文地球—地震学]