QuakeFlow: A Scalable Machine-learning-based Earthquake Monitoring Workflow with Cloud Computing
QuakeFlow: A Scalable Machine-learning-based Earthquake Monitoring Workflow with Cloud Computing
复制标题
QuakeFlow：基于云计算的可扩展的基于机器学习的地震监测工作流程
DOI：
--
复制
发表时间：
2022
影响因子：
2.8
通讯作者：
G. Beroza
中科院分区：
文献类型：
--
作者：
Weiqiang Zhu;A. Hou;Robert Yang;Avoy Datta;S. Mousavi;W. Ellsworth;G. Beroza
地震监测工作流程旨在检测地震信号并从连续波形数据中确定震源特征。深度学习地震学的最新发展已被用于改进地震监测工作流程中的任务，这些工作流程允许快速准确地检测比传统目录中多出几个数量级的小事件。为了促进机器学习算法在大规模地震记录中的应用，我们开发了一个基于云的地震监测工作流程，即MaskeFlow，它应用多个处理步骤从原始地震数据中生成地震目录。QuakeFlow使用深度学习模型PhaseNet来挑选P/S相位，并使用机器学习模型GaMMA来将相位与近似地震位置和震级相关联。QuakeFlow中的每个组件都是容器化的，允许使用新的深度学习/机器学习模型对管道进行直接更新，并且能够添加新组件，例如地震重定位算法。我们在Kubernetes中构建了Kubernetes Flow，使其能够自动扩展大型数据集，并使其易于部署在云平台上，从而实现大规模并行处理。我们使用SparkeFlow在几个小时内处理了来自波多黎各的三年连续存档数据，并发现了超过十倍的事件，这些事件发生在与以前已知的地震活动基本相同的结构上。我们应用地震流监测夏威夷的地震，发现了比标准目录中多一个数量级的事件，包括许多阐明岩浆系统深层结构的事件。我们还添加了Kafka和Spark流媒体，以提供实时地震监测结果。对于改进实时地震监测和挖掘存档的地震数据集来说，QuakeFlow是一种有效和高效的方法。
Earthquake monitoring workflows are designed to detect earthquake signals and to determine source characteristics from continuous waveform data. Recent developments in deep learning seismology have been used to improve tasks within earthquake monitoring workflows that allow the fast and accurate detection of up to orders of magnitude more small events than are present in conventional catalogs. To facilitate the application of machine-learning algorithms to large-volume seismic records at scale, we developed a cloud-based earthquake monitoring workflow, QuakeFlow, that applies multiple processing steps to generate earthquake catalogs from raw seismic data. QuakeFlow uses a deep learning model, PhaseNet, for picking P/S phases and a machine learning model, GaMMA, for phase association with approximate earthquake location and magnitude. Each component in QuakeFlow is containerized, allowing straightforward updates to the pipeline with new deep learning/machine learning models, as well as the ability to add new components, such as earthquake relocation algorithms. We built QuakeFlow in Kubernetes to make it auto-scale for large datasets and to make it easy to deploy on cloud platforms, which enables large-scale parallel processing. We used QuakeFlow to process three years of continuous archived data from Puerto Rico within a few hours, and found more than a factor of ten more events that occurred on much the same structures as previously known seismicity. We applied Quakeflow to monitoring earthquakes in Hawaii and found over an order of magnitude more events than are in the standard catalog, including many events that illuminate the deep structure of the magmatic system. We also added Kafka and Spark streaming to deliver real-time earthquake monitoring results. QuakeFlow is an effective and efficient approach both for improving realtime earthquake monitoring and for mining archived seismic data sets.
登录
查看更多内容
影响因子：
2.8
作者：
Zhu, Weiqiang;Beroza, Gregory C.
通讯作者：
Beroza, Gregory C.
影响因子：
3.9
作者：
Ross, Zachary E.;Yue, Yisong;Heaton, Thomas H.
通讯作者：
Heaton, Thomas H.
影响因子：
5.2
作者：
J. Gong;W. Fan;R. Parnell‐Turner
通讯作者：
J. Gong;W. Fan;R. Parnell‐Turner
影响因子：
3
作者：
Ross, Zachary E.;Meier, Men-Andrin;Heaton, Thomas H.
通讯作者：
Heaton, Thomas H.
莫纳克亚火山下二次沸腾产生的深部长周期地震
影响因子：
56.9
作者：
Wech, Aaron G.;Thelen, Weston A.;Thomas, Amanda M.
通讯作者：
Thomas, Amanda M.