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obspyDMT: A Python toolbox for retrieving and processing of large seismological datasets

机译:obspyDMT:一个Python工具箱,用于检索和处理大型地震数据集

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摘要

We present obspyDMT, a free, open source software toolbox for the query, retrieval, processing and management of seismological data sets, including very large, heterogeneous, and/or dynamically growing ones. obspyDMT simplifies and speeds up user-interaction with data centers, in more versatile ways than existing tools. The user is shielded from the complexities of interacting with different data centers and data exchange protocols, and is provided with powerful diagnostic and plotting tools to check the retrieved data and meta-data. While primarily a productivity tool for research seismologists and observatories, easy-to-use syntax and plotting functionality also make obspyDMT an effective teaching aid. Written in the Python programming language, it can be used as a stand-alone command line tool (requiring no knowledge of Python) or can be integrated as a module with other Python codes. It facilitates data archival, pre-processing, instrument correction, and quality control – routine but non-trivial tasks that can consume much user time.We describe obspyDMT’s functionality, design and technical implementation, accompanied by an overview of its use cases. As an example of a typical problem encountered in seismogram preprocessing, we show how to check for inconsistencies in response files of two example stations. We also demonstrate the fully automated request, remote computation, and retrieval of synthetic seismograms from IRIS DMC’s Syngine webservice.
机译:我们介绍obspyDMT,这是一个免费的开源软件工具箱,用于查询,检索,处理和管理地震数据集,包括非常大,异构和/或动态增长的数据集。与现有工具相比,obspyDMT以更通用的方式简化并加速了用户与数据中心的交互。用户免受与不同数据中心和数据交换协议进行交互的复杂性的困扰,并为用户提供了强大的诊断和绘图工具,以检查检索到的数据和元数据。虽然主要是研究地震学家和天文台的生产力工具,但易于使用的语法和绘图功能也使obspyDMT成为有效的教学辅助工具。它使用Python编程语言编写,可以用作独立的命令行工具(无需使用Python),也可以与其他Python代码集成为模块。它促进了数据归档,预处理,仪器校正和质量控制–这些日常但不琐碎的任务可能会占用大量用户时间。作为地震记录预处理中遇到的典型问题的示例,我们显示了如何检查两个示例站的响应文件中的不一致情况。我们还将演示IRIS DMC的Syngine网络服务对合成地震图的全自动请求,远程计算和检索。

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    Hosseini, K; Sigloch, K;

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  • 年度 2017
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